Dissertations / Theses: 'Network security intrusion detection'

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Maharjan, Nadim, and Paria Moazzemi. "Telemetry Network Intrusion Detection System." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581632.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California
Telemetry systems are migrating from links to networks. Security solutions that simply encrypt radio links no longer protect the network of Test Articles or the networks that support them. The use of network telemetry is dramatically expanding and new risks and vulnerabilities are challenging issues for telemetry networks. Most of these vulnerabilities are silent in nature and cannot be detected with simple tools such as traffic monitoring. The Intrusion Detection System (IDS) is a security mechanism suited to telemetry networks that can help detect abnormal behavior in the network. Our previous research in Network Intrusion Detection Systems focused on "Password" attacks and "Syn" attacks. This paper presents a generalized method that can detect both "Password" attack and "Syn" attack. In this paper, a K-means Clustering algorithm is used for vector quantization of network traffic. This reduces the scope of the problem by reducing the entropy of the network data. In addition, a Hidden-Markov Model (HMM) is then employed to help to further characterize and analyze the behavior of the network into states that can be labeled as normal, attack, or anomaly. Our experiments show that IDS can discover and expose telemetry network vulnerabilities using Vector Quantization and the Hidden Markov Model providing a more secure telemetry environment. Our paper shows how these can be generalized into a Network Intrusion system that can be deployed on telemetry networks.

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Abdullah, Kulsoom B. "Scaling and Visualizing Network Data to Facilitate in Intrusion Detection Tasks." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10509.

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As the trend of successful network attacks continue to rise, better forms of intrusion, detection and prevention are needed. This thesis addresses network traffic visualization techniques that aid administrators in recognizing attacks. A view of port statistics and Intrusion Detection System (IDS) alerts has been developed. Each help to address issues with analyzing large datasets involving networks. Due to the amount of traffic as well as the range of possible port numbers and IP addresses, scaling techniques are necessary. A port-based overview of network activity produces an improved representation for detecting and responding to malicious activity. We have found that presenting an overview using stacked histograms of aggregate port activity, combined with the ability to drill-down for finer details allows small, yet important details to be noticed and investigated without being obscured by large, usual traffic. Another problem administrators face is the cumbersome amount of alarm data generated from IDS sensors. As a result, important details are often overlooked, and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview from which system administrators can get a general sense of network activity and easily detect anomalies. They additionally have the option of then zooming and drilling down for details. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. For both of these systems, we describe the input data, the system design, and examples. Finally, we summarize potential future work.

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Yang, Yi. "Intrusion detection for communication network security in power systems." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.603572.

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In response to the emergence of cybersecurity issues in smarter grids, a number of IT security approaches have been presented. However, in practice, power networks with legacy systems are more difficult to update, patch and protect using conventional IT security techniques. This research presents a contribution to cybersecurity using Intrusion Detection Systems (IDS) in power systems. An intrusion detection methodology provides an approach to identify evidence of abnormal communication behaviours in a passive mode that does not impact normal operation of power systems but provides pre-emptive knowledge of potential threats and incidents. This thesis proposes and develops new intrusion detection approaches for Smart Grid cybersecurity that are applied in Supervisory Control and Data Acquisition (SCADA) and synchrophasor systems in order to monitor the operation of such systems and detect cyber threats against these systems resulting from malicious attacks or misuse by legitimate users. One of the proposed intrusion detection approaches combines whitelist categorisation with behaviour-based detection methods to identify known and unknown attacks by considering the operational features and the communication • protocols of SCADA and synchrophasor systems. Furthermore, SCADA-specific and synchrophasor-specific cybersecurity solutions are presented using test-beds to investigate, simulate and exemplify the impacts of cyber attacks on SCADA and synchrophasor systems. The proposed SCADA-specific IDS (SCADA-IDS) and Synchrophasor-Specific IDS (SSIDS) are implemented and verified using two lest-beds. In addition, a hybrid IDS is proposed for SCADA networks using the IEC 60870-5- 104 protocol, which contains signature-based, model-based and stateful detection methods. The proposed hybrid IDS is implemented and validated using the Internet Traffic and Content Analysis (ITACA) platform and the open source Snort tool. These new detection tools proposed in this thesis allow the cybersecurity of significant power systems communications networks to be improved, thus contribution 10 the security and reliability of the Smart Grid as a whole.

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Balupari, Ravindra. "Real-time network-based anomaly intrusion detection." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174579398.

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Ademi, Muhamet. "Web-Based Intrusion Detection System." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20271.

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Web applications are growing rapidly and as the amount of web sites globallyincreases so do security threats. Complex applications often interact with thirdparty services and databases to fetch information and often interactions requireuser input. Intruders are targeting web applications specifically and they are ahuge security threat to organizations and a way to combat this is to haveintrusion detection systems. Most common web attack methods are wellresearched and documented however due to time constraints developers oftenwrite applications fast and may not implement the best security practices. Thisreport describes one way to implement a intrusion detection system thatspecifically detects web based attacks.

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Park, Yongro. "A statistical process control approach for network intrusion detection." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6835.

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Intrusion detection systems (IDS) have a vital role in protecting computer networks and information systems. In this thesis we applied an SPC monitoring concept to a certain type of traffic data in order to detect a network intrusion. We developed a general SPC intrusion detection approach and described it and the source and the preparation of data used in this thesis. We extracted sample data sets that represent various situations, calculated event intensities for each situation, and stored these sample data sets in the data repository for use in future research. A regular batch mean chart was used to remove the sample datas inherent 60-second cycles. However, this proved too slow in detecting a signal because the regular batch mean chart only monitored the statistic at the end of the batch. To gain faster results, a modified batch mean (MBM) chart was developed that met this goal. Subsequently, we developed the Modified Batch Mean Shewhart chart, the Modified Batch Mean Cusum chart, and the Modified Batch Mean EWMA chart and analyzed the performances of each one on simulated data. The simulation studies showed that the MBM charts perform especially well with large signals ?the type of signal typically associated with a DOS intrusion. The MBM Charts can be applied two ways: by using actual control limits or by using robust control limits. The actual control limits must be determined by simulation, but the robust control limits require nothing more than the use of the recommended limits. The robust MBM Shewhart chart was developed based on choosing appropriate values based on batch size. The robust MBM Cusum chart and robust MBM EWMA chart were developed on choosing appropriate values of charting parameters.

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Stefanova, Zheni Svetoslavova. "Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7367.

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Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, the broader cybersecurity problem area has emerged as a significant concern, with many solution strategies being proposed for both intrusion detection and prevention. Now in general, the cybersecurity dilemma can be treated as a conflict-resolution setup entailing a security system and minimum of two decision agents with competing goals (e.g., the attacker and the defender). Namely, on the one hand, the defender is focused on guaranteeing that the system operates at or above an adequate (specified) level. Conversely, the attacker is focused on trying to interrupt or corrupt the system’s operation. In light of the above, this dissertation introduces novel methodologies to build appropriate strategies for system administrators (defenders). In particular, detailed mathematical models of security systems are developed to analyze overall performance and predict the likely behavior of the key decision makers influencing the protection structure. The initial objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks at a very early stage, i.e., in order to minimize potentially critical consequences and damage to system privacy and stability. Furthermore, another key objective is also to develop effective intrusion prevention (response) mechanisms. Along these lines, a machine learning based solution framework is developed consisting of two modules. Specifically, the first module prepares the system for analysis and detects whether or not there is a cyber-attack. Meanwhile, the second module analyzes the type of the breach and formulates an adequate response. Namely, a decision agent is used in the latter module to investigate the environment and make appropriate decisions in the case of uncertainty. This agent starts by conducting its analysis in a completely unknown milieu but continually learns to adjust its decision making based upon the provided feedback. The overall system is designed to operate in an automated manner without any intervention from administrators or other cybersecurity personnel. Human input is essentially only required to modify some key model (system) parameters and settings. Overall, the framework developed in this dissertation provides a solid foundation from which to develop improved threat detection and protection mechanisms for static setups, with further extensibility for handling streaming data.

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Huang, Yi-an. "Intrusion Detection and Response Systems for Mobile Ad Hoc Networks." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14053.

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A mobile ad hoc network (MANET) consists of a group of autonomous mobile nodes with no infrastructure support. In this research, we develop a distributed intrusion detection and response system for MANET, and we believe it presents a second line of defense that cannot be replaced by prevention schemes. We based our detection framework on the study of attack taxonomy. We then propose a set of detection methods suitable of detecting different attack categories. Our approaches are based on protocol specification analysis with categorical and statistical measures. Node-based approaches may be too restrictive in scenarios where attack patterns cannot be observed by any isolated node. Therefore, we have developed cooperative detection approaches for a more effective detection model. One approach is to form IDS clusters by grouping nearby nodes, and information can be exchanged within clusters. The cluster-based scheme is more efficient in terms of power consumption and resource utilization, it is also proved resilient against common security compromises without changing the decentralized assumption. We further address two response techniques, traceback and filtering. Existing traceback systems are not suitable for MANET because they rely on incompatible assumptions such as trustworthy routers and static route topology. Our solution, instead, adapts to dynamic topology with no infrastructure requirement. Our solution is also resilient in the face of arbitrary number of collaborative adversaries. We also develop smart filtering schemes to maximize the dropping rate of attack packets while minimizing the dropping rate of normal packets with real-time guarantee. To validate our research, we present case study using both ns-2 simulation and MobiEmu emulation platform with three ad hoc routing protocols: AODV, DSR and OLSR. We implemented various representative attacks based on the attack taxonomy. Our experiments show very promising results using node-based and cluster-based approaches.

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Pikoulas, John. "An agent-based Bayesian method for network intrusion detection." Thesis, Edinburgh Napier University, 2003. http://researchrepository.napier.ac.uk/Output/4057.

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Security is one of the major issues in any network and on the Internet. It encapsulates many different areas, such as protecting individual users against intruders, protecting corporate systems against damage, and protecting data from intrusion. It is obviously impossible to make a network totally secure, as there are so many areas that must be protected. This thesis includes an evaluation of current techniques for internal misuse of computer systems, and tries to propose a new way of dealing with this problem. This thesis proposes that it is impossible to fully protect a computer network from intrusion, and shows how different methods are applied at differing levels of the OSI model. Most systems are now protected at the network and transport layer, with systems such as firewalls and secure sockets. A weakness, though, exists in the session layer that is responsible for user logon and their associated password. It is thus important for any highly secure system to be able to continually monitor a user, even after they have successfully logged into the system. This is because once an intruder has successfully logged into a system, they can use it as a stepping-stone to gain full access (often right up to the system administrator level). This type of login identifies another weakness of current intrusion detection systems, in that they are mainly focused on detecting external intrusion, whereas a great deal of research identifies that one of the main problems is from internal intruders, and from staff within an organisation. Fraudulent activities can often he identified by changes in user behaviour. While this type of behaviour monitoring might not be suited to most networks, it could be applied to high secure installations, such as in government, and military organisations. Computer networks are now one of the most rapidly changing and vulnerable systems, where security is now a major issue. A dynamic approach, with the capacity to deal with and adapt to abrupt changes, and be simple, will provide an effective modelling toolkit. Analysts must be able to understand how it works and be able to apply it without the aid of an expert. Such models do exist in the statistical world, and it is the purpose of this thesis to introduce them and to explain their basic notions and structure. One weakness identified is the centralisation and complex implementation of intrusion detection. The thesis proposes an agent-based approach to monitor the user behaviour of each user. It also proposes that many intrusion detection systems cannot cope with new types of intrusion. It thus applies Bayesian statistics to evaluate user behaviour, and predict the future behaviour of the user. The model developed is a unique application of Bayesian statistics, and the results show that it can improve future behaviour prediction than existing ARIMA models. The thesis argues that the accuracy of long-term forecasting questionable, especially in systems that have a rapid and often unexpected evolution and behaviour. Many of the existing models for prediction use long-term forecasting, which may not be the optimal type for intrusion detection systems. The experiments conducted have varied the number of users and the time interval used for monitoring user behaviour. These results have been compared with ARIMA, and an increased accuracy has been observed. The thesis also shows that the new model can better predict changes in user behaviour, which is a key factor in identifying intrusion detection. The thesis concludes with recommendations for future work, including how the statistical model could be improved. This includes research into changing the specification of the design vector for Bayesian. Another interesting area is the integration of standard agent communication agents, which will make the security agents more social in their approach and be able to gather information from other agents

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Haas, Steffen [Verfasser]. "Security Monitoring and Alert Correlation for Network Intrusion Detection / Steffen Haas." Hamburg : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2020. http://d-nb.info/123199780X/34.

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Freet, David Nathan. "A Security Visualization Analysis Methodology for Improving Network Intrusion Detection Efficiency." Thesis, Indiana State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10261868.

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The flood of raw data generated by intrusion detection and other network monitoring devices can be so overwhelming that it causes great difficulty in detecting patterns that might indicate malicious traffic. In order to more effectively monitor and process network and forensic data within a virtualized environment, Security Visualization (SecViz) provides software-based visual interfaces to analyze live and logged network data within the domains of network security, network and cloud forensics, attack prevention, compliance management, wireless security, secure coding, and penetration testing. Modern networks generate enormous amounts of data that is often stored in logs. Due to the lack of effective approaches to organizing and visualizing log data, most network monitoring tools focus at a high level on data throughput and efficiency, or dig too far down into the packet level to allow for useful analysis by network administrators. SecViz offers a simpler and more effective approach to analyzing the massive amounts of log data generated on a regular basis. Graphical representations make it possible to identify and detect malicious activity, and spot general trends and relationships among individual data points. The human brain can rapidly process visual information in a detailed and meaningful manner. By converting network security and forensic data into a human-readable picture, SecViz can address and solve complex data analysis challenges and significantly increase the efficiency by which data is processed by information security professionals.

This study utilizes the Snort intrusion detection system and SecViz tools to monitor and analyze various attack scenarios in a virtualized cloud computing environment. Real-time attacks are conducted in order to generate traffic and log data that can then be re-played in a number of software applications for analysis. A Java-based program is written to aggregate and display Snort data, and then incorporated into a custom Linux-based software environment along with select open-source SecViz tools. A methodology is developed to correlate Snort intrusion alerts with log data in order to create a visual picture that can significantly enhance the identification of malicious network activity and discrimination from normal traffic within a virtualized cloud-based network.

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Jacoby, Grant A. "Battery-based intrusion detection /." This resource online, 2005. http://scholar.lib.vt.edu/theses/available/etd-04212005-120840.

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Ewell, Cris Vincent. "Detection of Deviations From Authorized Network Activity Using Dynamic Bayesian Networks." NSUWorks, 2011. http://nsuworks.nova.edu/gscis_etd/146.

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This research addressed one of the hard problems still plaguing the information security profession; detection of network activity deviations from authorized accounts when the deviations are similar to normal network activity. Specifically, when user and administrator type accounts are used for malicious activity, harm can come to the organization. Accurately modeling normal user network activity is hard to accomplish and detecting misuse is a complex problem. Much work has been done in the past with intrusion detection systems, but being able to detect masquerade events with high accuracy and low false alarm rates continues to be an issue. Bayesian networks have been successfully used in the past to reason under certainty by combining prior knowledge with observed data. The use of dynamic Bayesian Networks, such as multi-entity Bayesian network, extends the capability and can address complex problems. The goal of the research was to extend previous research with multi-entity Bayesian networks along with discretization methods to improve the effectiveness of the detection rate while maintaining an acceptable level of false positives. Preprocessing continuous variables has proven effective in prior research but has not been applied to multi-entity Bayesian networks in the past. Five different discretization methods were used in this research. Analysis using receiver operating characteristic curves, confusion matrix, and other comparison methods were completed as part of this research. The results of the research demonstrated that a multi-entity Bayesian network model based on multiple data sources and the relationship between the user attributes could be used to detect unauthorized access to data. The supervised top down discretization methods had better performance related to the overall classification accuracy. Specifically, the class-attribute interdependence maximization discretization method outperformed the other four discretization methods. When compared to previous masquerade detection methods, the class-attribute interdependence maximization discretization method had a comparable true positive rate with a lower false positive rate.

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Zhang, Junjie. "Effective and scalable botnet detection in network traffic." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44837.

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Botnets represent one of the most serious threats against Internet security since they serve as platforms that are responsible for the vast majority of large-scale and coordinated cyber attacks, such as distributed denial of service, spamming, and information stolen. Detecting botnets is therefore of great importance and a number of network-based botnet detection systems have been proposed. However, as botnets perform attacks in an increasingly stealthy way and the volume of network traffic is rapidly growing, existing botnet detection systems are faced with significant challenges in terms of effectiveness and scalability. The objective of this dissertation is to build novel network-based solutions that can boost both the effectiveness of existing botnet detection systems by detecting botnets whose attacks are very hard to be observed in network traffic, and their scalability by adaptively sampling network packets that are likely to be generated by botnets. To be specific, this dissertation describes three unique contributions. First, we built a new system to detect drive-by download attacks, which represent one of the most significant and popular methods for botnet infection. The goal of our system is to boost the effectiveness of existing drive-by download detection systems by detecting a large number of drive-by download attacks that are missed by these existing detection efforts. Second, we built a new system to detect botnets with peer-to-peer (P2P) command&control (C&C) structures (i.e., P2P botnets), where P2P C&Cs represent currently the most robust C&C structures against disruption efforts. Our system aims to boost the effectiveness of existing P2P botnet detection by detecting P2P botnets in two challenging scenarios: i) botnets perform stealthy attacks that are extremely hard to be observed in the network traffic; ii) bot-infected hosts are also running legitimate P2P applications (e.g., Bittorrent and Skype). Finally, we built a novel traffic analysis framework to boost the scalability of existing botnet detection systems. Our framework can effectively and efficiently identify a small percentage of hosts that are likely to be bots, and then forward network traffic associated with these hosts to existing detection systems for fine-grained analysis, thereby boosting the scalability of existing detection systems. Our traffic analysis framework includes a novel botnet-aware and adaptive packet sampling algorithm, and a scalable flow-correlation technique.

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Yellapragada, Ramani. "Probabilistic Model for Detecting Network Traffic Anomalies." Ohio University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1088538020.

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Tevemark, Jonas. "Intrusion Detection and Prevention in IP Based Mobile Networks." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12015.

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Ericsson’s Packet Radio Access Network (PRAN) is a network solution for packet transport in mobile networks, which utilizes the Internet Protocol (IP). The IP protocol offers benefits in responsiveness and performance adaptation to data bursts when compared to Asynchronous Transfer Mode (ATM), which is still often used. There are many manufacturers / operators providing IP services, which reduce costs. The IP’s use on the Internet brings greater end-user knowledge, wider user community and more programs designed for use in IP environments. Because of this, the spectrum of possible attacks against PRAN broadens. This thesis provides information on what protection an Intrusion Prevention System (IPS) can add to the current PRAN solution.

A risk analysis is performed to identify assets in and threats against PRAN, and to discover attacks that can be mitigated by the use of an IPS. Information regarding placement of an IPS in the PRAN network is given and tests of a candidate system are performed. IPS features in hardware currently used by Ericsson as well as missing features are pinpointed . Finally, requirements for an IPS intended for use in PRAN are concluded.

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Asarcıklı, Şükran Tuğlular Tuğkan. "Firewall monitoring using intrusion detection systems/." [s.l.]: [s.n.], 2005. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000390.pdf.

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Clark, Christopher R. "Design of Efficient FPGA Circuits For Matching Complex Patterns in Network Intrusion Detection Systems." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5137.

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The objective of this research is to design and develop a reconfigurable string matching co-processor using field-programmable gate array (FPGA) technology that is capable of matching thousands of complex patterns at gigabit network rates for network intrusion detection systems (NIDS). The motivation for this work is to eliminate the most significant bottleneck in current NIDS software, which is the pattern matching process. The tasks involved with this research include designing efficient, high-performance hardware circuits for pattern matching and integrating the pattern matching co-processor with other NIDS components running on a network processor. The products of this work include a system to translate standard intrusion detection patterns to FPGA pattern matching circuits that support all the functionality required by modern NIDS. The system generates circuits efficient enough to enable the entire ruleset of a popular NIDS containing over 1,500 patterns and 17,000 characters to fit into a single low-end FPGA chip and process data at an input rate of over 800 Mb/s. The capacity and throughput both scale linearly, so larger and faster FPGA devices can be used to further increase performance. The FPGA co-processor allows the task of pattern matching to be completely offloaded from a NIDS, significantly improving the overall performance of the system.

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Fogla, Prahlad. "Improving the Efficiency and Robustness of Intrusion Detection Systems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19772.

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With the increase in the complexity of computer systems, existing security measures are not enough to prevent attacks. Intrusion detection systems have become an integral part of computer security to detect attempted intrusions. Intrusion detection systems need to be fast in order to detect intrusions in real time. Furthermore, intrusion detection systems need to be robust against the attacks which are disguised to evade them. We improve the runtime complexity and space requirements of a host-based anomaly detection system that uses q-gram matching. q-gram matching is often used for approximate substring matching problems in a wide range of application areas, including intrusion detection. During the text pre-processing phase, we store all the q-grams present in the text in a tree. We use a tree redundancy pruning algorithm to reduce the size of the tree without losing any information. We also use suffix links for fast linear-time q-gram search during query matching. We compare our work with the Rabin-Karp based hash-table technique, commonly used for multiple q-gram matching. To analyze the robustness of network anomaly detection systems, we develop a new class of polymorphic attacks called polymorphic blending attacks, that can effectively evade payload-based network anomaly IDSs by carefully matching the statistics of the mutated attack instances to the normal profile. Using PAYL anomaly detection system for our case study, we show that these attacks are practically feasible. We develop a formal framework which is used to analyze polymorphic blending attacks for several network anomaly detection systems. We show that generating an optimal polymorphic blending attack is NP-hard for these anomaly detection systems. However, we can generate polymorphic blending attacks using the proposed approximation algorithms. The framework can also be used to improve the robustness of an intrusion detector. We suggest some possible countermeasures one can take to improve the robustness of an intrusion detection system against polymorphic blending attacks.

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Lee, Robert. "ON THE APPLICATION OF LOCALITY TO NETWORK INTRUSION DETECTION: WORKING-SET ANALYSIS OF REAL AND SYNTHETIC NETWORK SERVER TRAFFIC." Doctoral diss., Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002718.

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DENG, HONGMEI. "AN INTEGRATED SECURITY SCHEME WITH RESOURCE-AWARENESS FOR WIRELESS AD HOC NETWORKS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1091454944.

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Langin, Chester Louis. "A SOM+ Diagnostic System for Network Intrusion Detection." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/389.

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This research created a new theoretical Soft Computing (SC) hybridized network intrusion detection diagnostic system including complex hybridization of a 3D full color Self-Organizing Map (SOM), Artificial Immune System Danger Theory (AISDT), and a Fuzzy Inference System (FIS). This SOM+ diagnostic archetype includes newly defined intrusion types to facilitate diagnostic analysis, a descriptive computational model, and an Invisible Mobile Network Bridge (IMNB) to collect data, while maintaining compatibility with traditional packet analysis. This system is modular, multitaskable, scalable, intuitive, adaptable to quickly changing scenarios, and uses relatively few resources.

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Mantere, M. (Matti). "Network security monitoring and anomaly detection in industrial control system networks." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526208152.

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Abstract Industrial control system (ICS) networks used to be isolated environments, typically separated by physical air gaps from the wider area networks. This situation has been changing and the change has brought with it new cybersecurity issues. The process has also exacerbated existing problems that were previously less exposed due to the systems’ relative isolation. This process of increasing connectivity between devices, systems and persons can be seen as part of a paradigm shift called the Internet of Things (IoT). This change is progressing and the industry actors need to take it into account when working to improve the cybersecurity of ICS environments and thus their reliability. Ensuring that proper security processes and mechanisms are being implemented and enforced on the ICS network level is an important part of the general security posture of any given industrial actor. Network security and the detection of intrusions and anomalies in the context of ICS networks are the main high-level research foci of this thesis. These issues are investigated through work on machine learning (ML) based anomaly detection (AD). Potentially suitable features, approaches and algorithms for implementing a network anomaly detection system for use in ICS environments are investigated. After investigating the challenges, different approaches and methods, a proof-ofconcept (PoC) was implemented. The PoC implementation is built on top of the Bro network security monitoring framework (Bro) for testing the selected approach and tools. In the PoC, a Self-Organizing Map (SOM) algorithm is implemented using Bro scripting language to demonstrate the feasibility of using Bro as a base system. The implemented approach also represents a minimal case of event-driven machine learning anomaly detection (EMLAD) concept conceived during the research. The contributions of this thesis are as follows: a set of potential features for use in machine learning anomaly detection, proof of the feasibility of the machine learning approach in ICS network setting, a concept for event-driven machine learning anomaly detection, a design and initial implementation of user configurable and extendable machine learning anomaly detection framework for ICS networks
Tiivistelmä Kehittyneet yhteiskunnat käyttävät teollisuuslaitoksissaan ja infrastruktuuriensa operoinnissa monimuotoisia automaatiojärjestelmiä. Näiden automaatiojärjestelmien tieto- ja kyberturvallisuuden tila on hyvin vaihtelevaa. Laitokset ja niiden hyödyntämät järjestelmät voivat edustaa usean eri aikakauden tekniikkaa ja sisältää useiden eri aikakauden heikkouksia ja haavoittuvaisuuksia. Järjestelmät olivat aiemmin suhteellisen eristyksissä muista tietoverkoista kuin omista kommunikaatioväylistään. Tämä automaatiojärjestelmien eristyneisyyden heikkeneminen on luonut uuden joukon uhkia paljastamalla niiden kommunikaatiorajapintoja ympäröivälle maailmalle. Nämä verkkoympäristöt ovat kuitenkin edelleen verrattaen eristyneitä ja tätä ominaisuutta voidaan hyödyntää niiden valvonnassa. Tässä työssä esitetään tutkimustuloksia näiden verkkojen turvallisuuden valvomisesta erityisesti poikkeamien havainnoinnilla käyttäen hyväksi koneoppimismenetelmiä. Alkuvaiheen haasteiden ja erityispiirteiden tutkimuksen jälkeen työssä käytetään itsejärjestyvien karttojen (Self-Organizing Map, SOM) algoritmia esimerkkiratkaisun toteutuksessa uuden konseptin havainnollistamiseksi. Tämä uusi konsepti on tapahtumapohjainen koneoppiva poikkeamien havainnointi (Event-Driven Machine Learning Anomaly Detection, EMLAD). Työn kontribuutiot ovat seuraavat, kaikki teollisuusautomaatioverkkojen kontekstissa: ehdotus yhdeksi anomalioiden havainnoinnissa käytettävien ominaisuuksien ryhmäksi, koneoppivan poikkeamien havainnoinnin käyttökelpoisuuden toteaminen, laajennettava ja joustava esimerkkitoteutus uudesta EMLAD-konseptista toteutettuna Bro NSM työkalun ohjelmointikielellä

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Li, Zhe. "A Neural Network Based Distributed Intrusion Detection System on Cloud Platform." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1364835027.

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Labonne, Maxime. "Anomaly-based network intrusion detection using machine learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS011.

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Ces dernières années, le piratage est devenu une industrie à part entière, augmentant le nombre et la diversité des cyberattaques. Les menaces qui pèsent sur les réseaux informatiques vont des logiciels malveillants aux attaques par déni de service, en passant par le phishing et l'ingénierie sociale. Un plan de cybersécurité efficace ne peut plus reposer uniquement sur des antivirus et des pare-feux pour contrer ces menaces : il doit inclure plusieurs niveaux de défense. Les systèmes de détection d'intrusion (IDS) réseaux sont un moyen complémentaire de renforcer la sécurité, avec la possibilité de surveiller les paquets de la couche 2 (liaison) à la couche 7 (application) du modèle OSI. Les techniques de détection d'intrusion sont traditionnellement divisées en deux catégories : la détection par signatures et la détection par anomalies. La plupart des IDS utilisés aujourd'hui reposent sur la détection par signatures ; ils ne peuvent cependant détecter que des attaques connues. Les IDS utilisant la détection par anomalies sont capables de détecter des attaques inconnues, mais sont malheureusement moins précis, ce qui génère un grand nombre de fausses alertes. Dans ce contexte, la création d'IDS précis par anomalies est d'un intérêt majeur pour pouvoir identifier des attaques encore inconnues.Dans cette thèse, les modèles d'apprentissage automatique sont étudiés pour créer des IDS qui peuvent être déployés dans de véritables réseaux informatiques. Tout d'abord, une méthode d'optimisation en trois étapes est proposée pour améliorer la qualité de la détection : 1/ augmentation des données pour rééquilibrer les jeux de données, 2/ optimisation des paramètres pour améliorer les performances du modèle et 3/ apprentissage ensembliste pour combiner les résultats des meilleurs modèles. Les flux détectés comme des attaques peuvent être analysés pour générer des signatures afin d'alimenter les bases de données d'IDS basées par signatures. Toutefois, cette méthode présente l'inconvénient d'exiger des jeux de données étiquetés, qui sont rarement disponibles dans des situations réelles. L'apprentissage par transfert est donc étudié afin d'entraîner des modèles d'apprentissage automatique sur de grands ensembles de données étiquetés, puis de les affiner sur le trafic normal du réseau à surveiller. Cette méthode présente également des défauts puisque les modèles apprennent à partir d'attaques déjà connues, et n'effectuent donc pas réellement de détection d'anomalies. C'est pourquoi une nouvelle solution basée sur l'apprentissage non supervisé est proposée. Elle utilise l'analyse de l'en-tête des protocoles réseau pour modéliser le comportement normal du trafic. Les anomalies détectées sont ensuite regroupées en attaques ou ignorées lorsqu'elles sont isolées. Enfin, la détection la congestion réseau est étudiée. Le taux d'utilisation de la bande passante entre les différents liens est prédit afin de corriger les problèmes avant qu'ils ne se produisent
In recent years, hacking has become an industry unto itself, increasing the number and diversity of cyber attacks. Threats on computer networks range from malware to denial of service attacks, phishing and social engineering. An effective cyber security plan can no longer rely solely on antiviruses and firewalls to counter these threats: it must include several layers of defence. Network-based Intrusion Detection Systems (IDSs) are a complementary means of enhancing security, with the ability to monitor packets from OSI layer 2 (Data link) to layer 7 (Application). Intrusion detection techniques are traditionally divided into two categories: signatured-based (or misuse) detection and anomaly detection. Most IDSs in use today rely on signature-based detection; however, they can only detect known attacks. IDSs using anomaly detection are able to detect unknown attacks, but are unfortunately less accurate, which generates a large number of false alarms. In this context, the creation of precise anomaly-based IDS is of great value in order to be able to identify attacks that are still unknown.In this thesis, machine learning models are studied to create IDSs that can be deployed in real computer networks. Firstly, a three-step optimization method is proposed to improve the quality of detection: 1/ data augmentation to rebalance the dataset, 2/ parameters optimization to improve the model performance and 3/ ensemble learning to combine the results of the best models. Flows detected as attacks can be analyzed to generate signatures to feed signature-based IDS databases. However, this method has the disadvantage of requiring labelled datasets, which are rarely available in real-life situations. Transfer learning is therefore studied in order to train machine learning models on large labeled datasets, then finetune them on benign traffic of the network to be monitored. This method also has flaws since the models learn from already known attacks, and therefore do not actually perform anomaly detection. Thus, a new solution based on unsupervised learning is proposed. It uses network protocol header analysis to model normal traffic behavior. Anomalies detected are then aggregated into attacks or ignored when isolated. Finally, the detection of network congestion is studied. The bandwidth utilization between different links is predicted in order to correct issues before they occur

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Botes, Frans Hendrik. "Ant tree miner amyntas for intrusion detection." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2865.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2018.
With the constant evolution of information systems, companies have to acclimatise to the vast increase of data flowing through their networks. Business processes rely heavily on information technology and operate within a framework of little to no space for interruptions. Cyber attacks aimed at interrupting business operations, false intrusion detections and leaked information burden companies with large monetary and reputational costs. Intrusion detection systems analyse network traffic to identify suspicious patterns that intent to compromise the system. Classifiers (algorithms) are used to classify the data within different categories e.g. malicious or normal network traffic. Recent surveys within intrusion detection highlight the need for improved detection techniques and warrant further experimentation for improvement. This experimental research project focuses on implementing swarm intelligence techniques within the intrusion detection domain. The Ant Tree Miner algorithm induces decision trees by using ant colony optimisation techniques. The Ant Tree Miner poses high accuracy with efficient results. However, limited research has been performed on this classifier in other domains such as intrusion detection. The research provides the intrusion detection domain with a new algorithm that improves upon results of decision trees and ant colony optimisation techniques when applied to the domain. The research has led to valuable insights into the Ant Tree Miner classifier within a previously unknown domain and created an intrusion detection benchmark for future researchers.

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Sawant, Ankush. "Time-based Approach to Intrusion Detection using Multiple Self-Organizing Maps." Ohio University / OhioLINK, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1113833809.

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Sohal, Amandeep Kaur. "A taxonomy-based approach to intrusion detection system." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1446428.

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Nwanze, Nnamdi Chike. "Anomaly-based intrusion detection using using lightweight stateless payload inspection." Diss., Online access via UMI:, 2009.

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Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2009.
Includes bibliographical references.

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Tarim, Mehmet Cem. "A Faster Intrusion Detection Method For High-speed Computer Networks." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613246/index.pdf.

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The malicious intrusions to computer systems result in the loss of money, time and hidden information which require deployment of intrusion detection systems. Existing intrusion detection methods analyze packet payload to search for certain strings and to match them with a rule database which takes a long time in large size packets. Because of buffer limits, packets may be dropped or the system may stop working due to high CPU load. In this thesis, we investigate signature based intrusion detection with signatures that only depend on the packet header information without payload inspection. To this end, we analyze the well-known DARPA 1998 dataset to manually extract such signatures and construct a new rule set to detect the intrusions. We implement our rule set in a popular intrusion detection software tool, Snort. Furthermore we enhance our rule set with the existing rules of Snort which do not depend on payload inspection. We test our rule set on DARPA data set as well as a new data set that we collect using attack generator tools. Our results show around 30% decrease in detection time with a tolerable decrease in the detection rate. We believe that our method can be used as a complementary component to speed up intrusion detection systems.

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Yüksel, Ulaş Tuğlular Tuğkan. "Development of a Quality Assurance Prototype for Intrusion Detection Systems/." [s.l.]: [s.n.], 2002. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000131.pdf.

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Taub, Lawrence. "Application of a Layered Hidden Markov Model in the Detection of Network Attacks." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/320.

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Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload's contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates.

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Taylor, Adrian. "Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36120.

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Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, attackers can access a car's controller area network (CAN) bus and cause malicious effects. We seek to detect these attacks on the bus as a last line of defence against automotive cyber attacks. The CAN bus standard defines a low-level message structure, upon which manufacturers layer their own proprietary command protocols; attacks must similarly be tailored for their target. This variability makes intrusion detection methods difficult to apply to the automotive CAN bus. Nevertheless, the bus traffic is generated by machines; thus we hypothesize that it can be characterized with machine learning, and that attacks produce anomalous traffic. Our goals are to show that anomaly detection trained without understanding of the message contents can detect attacks, and to create a framework for understanding how the characteristics of a novel attack can be used to predict its detectability. We developed a model that describes attacks based on their effect on bus traffic, informed by a review of published material on car hacking in combination with analysis of CAN traffic from a 2012 Subaru Impreza. The model specifies three high-level categories of effects: attacks that insert foreign packets, attacks that affect packet timing, and attacks that only modify data within packets. Foreign packet attacks are trivially detectable. For timing-based anomalies, we developed features suitable for one-class classification methods. For packet stream data word anomalies, we adapted recurrent neural networks and multivariate Markov model methods to sequence anomaly detection and compared their performance. We conducted experiments to evaluate our detection methods with special attention to the trade-off between precision and recall, given that a practical system requires a very low false alarm rate. The methods were evaluated by synthesizing anomalies within each attack category, parameterized to adjust their covertness. We generalize from the results to enable prediction of detection rates for new attacks using these methods.

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Techateerawat, Piya, and piyat33@yahoo com. "Key distribution and distributed intrusion detection system in wireless sensor network." RMIT University. Electrical and Computer Systems Engineering, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080729.162610.

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This thesis proposes a security solution in key management and Intrusion Detection System (IDS) for wireless sensor networks. It addresses challenges of designing in energy and security requirement. Since wireless communication consumes the most energy in sensor network, transmissions must be used efficiently. We propose Hint Key Distribution (HKD) for key management and Adaptive IDS for distributing activated IDS nodes and cooperative operation of these two protocols. HKD protocol focuses on the challenges of energy, computation and security. It uses a hint message and key chain to consume less energy while self-generating key can secure the secret key. It is a proposed solution to key distribution in sensor networks. Adaptive IDS uses threshold and voting algorithm to distribute IDS through the network. An elected node is activated IDS to monitor its network and neighbors. A threshold is used as a solution to reduce number of repeated activations of the same node. We attempt to distribute the energy use equally across the network. In a cooperative protocol, HKD and Adaptive IDS exchange information in order to adjust to the current situation. The level of alert controls the nature of the interaction between the two protocols.

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Jieke, Pan. "Cooperative Intrusion Detection For The Next Generation Carrier Ethernet." Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/13881.

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Current OSI model layer 2 network elements (NEs, e.g., bridges, switches) are complex hardware and software boxes, often running an operating system, service and administration software, that can be vulnerable to attacks, including to remote code execution inside them. The purpose of this thesis is to present an architecture to protect the Carrier Ethernet network infrastructure from attacks performed by malicious NEs against the link management protocol, Spanning Tree Protocol, and its variations. This thesis proposes that NEs are equipped with an intrusion detection component. Each detector uses a specification-based intrusion detection mechanism in order to inspect the behaviour of other NEs through the analysis of the received messages. The correct behaviour of the NEs is crafted from the standard specification of the STP protocol. If there is a deviation between current and expected behaviour, then the NE is considered to be malicious. The specification is extended with temporal pattern annotations, in order to detect certain deviations from the protocol. The results of the local detection are then transmitted to the other NEs, in order to cooperatively establish a correlation between all the NEs, so that malicious NEs can be logically removed from the network (disconnecting the ports connected to them)

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Satam, Pratik. "An Anomaly Behavior Analysis Intrusion Detection System for Wireless Networks." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/595654.

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Wireless networks have become ubiquitous, where a wide range of mobile devices are connected to a larger network like the Internet via wireless communications. One widely used wireless communication standard is the IEEE 802.11 protocol, popularly called Wi-Fi. Over the years, the 802.11 has been upgraded to different versions. But most of these upgrades have been focused on the improvement of the throughput of the protocol and not enhancing the security of the protocol, thus leaving the protocol vulnerable to attacks. The goal of this research is to develop and implement an intrusion detection system based on anomaly behavior analysis that can detect accurately attacks on the Wi-Fi networks and track the location of the attacker. As a part of this thesis we present two architectures to develop an anomaly based intrusion detection system for single access point and distributed Wi-Fi networks. These architectures can detect attacks on Wi-Fi networks, classify the attacks and track the location of the attacker once the attack has been detected. The system uses statistical and probability techniques associated with temporal wireless protocol transitions, that we refer to as Wireless Flows (Wflows). The Wflows are modeled and stored as a sequence of n-grams within a given period of analysis. We studied two approaches to track the location of the attacker. In the first approach, we use a clustering approach to generate power maps that can be used to track the location of the user accessing the Wi-Fi network. In the second approach, we use classification algorithms to track the location of the user from a Central Controller Unit. Experimental results show that the attack detection and classification algorithms generate no false positives and no false negatives even when the Wi-Fi network has high frame drop rates. The Clustering approach for location tracking was found to perform highly accurate in static environments (81% accuracy) but the performance rapidly deteriorates with the changes in the environment. While the classification algorithm to track the location of the user at the Central Controller/RADIUS server was seen to perform with lesser accuracy then the clustering approach (76% accuracy) but the system's ability to track the location of the user deteriorated less rapidly with changes in the operating environment.

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Pattam, Shoban. "Enhancing Security in 802.11 and 802.1 X Networks with Intrusion Detection." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/1034.

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The convenience and low cost of 802.11-based Wireless Local Area Networks (WLANs) complemented with 802.1 X authentication has led to widespread deployment in the consumer, industrial and military sectors. The combination of wireless signals radiating further than the intended coverage area, flaws in 802.11's basic security mechanisms and vulnerabilities found in 802.1 X have been widely publicized. Military bases and navy ships are open targets for wireless attacks. Wireless Intrusion Detection System (WIDS), provides an additional (external) layer of security by combining intrusion detection, fire walling, packet filtering and determining the physical location of the intruder.

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Fragkos, Grigorios. "Near real-time threat assessment using intrusion detection system's data." Thesis, University of South Wales, 2011. https://pure.southwales.ac.uk/en/studentthesis/near-realtime-threat-assessment-using-intrusion-detection-systems-data(96a9528f-f319-4125-aaf0-71593bb61b56).html.

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The concept of Intrusion Detection (ID) and the development of such systems have been a major concern for scientists since the late sixties. In recent computer networks, the use of different types of Intrusion Detection Systems (IDS) is considered essential and in most cases mandatory. Major improvements have been achieved over the years and a large number of different approaches have been developed and applied in the way these systems perform Intrusion Detection. The purpose of the research is to introduce a novel approach that will enable us to take advantage of the vast amounts of information generated by the large number of different IDSs, in order to identify suspicious traffic, malicious intentions and network attacks in an automated manner. In order to achieve this, the research focuses upon a system capable of identifying malicious activity in near real-time, that is capable of identifying attacks while they are progressing. The thesis addresses the near real-time threat assessment by researching into current state of the art solutions. Based on the literature review, current Intrusion Detection technologies lean towards event correlation systems using different types of detections techniques. Instead of using linear event signatures or rule sets, the thesis suggests a structured description of network attacks based on the abstracted form of the attacker’s activity. For that reason, the design focuses upon the description of network attacks using the development of footprints. Despite the level of knowledge, capabilities and resources of the attacker, the system compares occurring network events against predefined footprints in order to identify potential malicious activity. Furthermore, based on the implementation of the footprints, the research also focuses upon the design of the Threat Assessment Engine (TAE) which is capable of performing detection in near real-time by the use of the above described footprints. The outcome of the research proves that it is possible to have an automated process performing threat assessment despite the number of different ongoing attacks taking place simultaneously. The threat assessment process, taking into consideration the system’s architecture, is capable of acting as the human analyst would do when investigating such network activity. This automation speeds up the time-consuming process of manually analysing and comparing data logs deriving from heterogeneous sources, as it performs the task in near real-time. Effectively, by performing the this task in near real-time, the proposed system is capable of detecting complicated malicious activity which in other cases, as currently performed, it would be difficult, maybe impossible or results would be generated too late.

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Ranang, Martin Thorsen. "An Artificial Immune System Approach to Preserving Security in Computer Networks." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-255.

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It is believed that many of the mechanisms present in the biological immune system are well suited for adoption to the field of computer intrusion detection, in the form of artificial immune systems. In this report mechanisms in the biological immune system are introduced, their parallels in artificial immune systems are presented, and how they may be applied to intrusion detection in a computer environment is discussed. An artificial immune system is designed, implemented and applied to detect intrusive behavior in real network data in a simulated network environment. The effect of costimulation and clonal proliferation combined with somatic hypermutation to perform affinity maturation of detectors in the artificial immune system is explored through experiments. An exact expression for the probability of a match between two randomly chosen strings using the r-contiguous matching rule is developed. The use of affinity maturation makes it possible to perform anomaly detection by using smaller sets of detectors with a high level of specificity while maintaining a high level of cover and diversity, which increases the number of true positives, while keeping a low level of false negatives.

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Zhang, Tao. "RADAR: compiler and architecture supported intrusion prevention, detection, analysis and recovery." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-08042006-122745/.

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Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2007.
Ahamad, Mustaque, Committee Member ; Pande, Santosh, Committee Chair ; Lee, Wenke, Committee Member ; Schwan, Karsten, Committee Member ; Yang, Jun, Committee Member.

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Siddiqui, Abdul Jabbar. "Securing Connected and Automated Surveillance Systems Against Network Intrusions and Adversarial Attacks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42345.

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In the recent years, connected surveillance systems have been witnessing an unprecedented evolution owing to the advancements in internet of things and deep learning technologies. However, vulnerabilities to various kinds of attacks both at the cyber network-level and at the physical worldlevel are also rising. This poses danger not only to the devices but also to human life and property. The goal of this thesis is to enhance the security of an internet of things, focusing on connected video-based surveillance systems, by proposing multiple novel solutions to address security issues at the cyber network-level and to defend such systems at the physical world-level. In order to enhance security at the cyber network-level, this thesis designs and develops solutions to detect network intrusions in an internet of things such as surveillance cameras. The first solution is a novel method for network flow features transformation, named TempoCode. It introduces a temporal codebook-based encoding of flow features based on capturing the key patterns of benign traffic in a learnt temporal codebook. The second solution takes an unsupervised learning-based approach and proposes four methods to build efficient and adaptive ensembles of neural networks-based autoencoders for intrusion detection in internet of things such as surveillance cameras. To address the physical world-level attacks, this thesis studies, for the first time to the best of our knowledge, adversarial patches-based attacks against a convolutional neural network (CNN)- based surveillance system designed for vehicle make and model recognition (VMMR). The connected video-based surveillance systems that are based on deep learning models such as CNNs are highly vulnerable to adversarial machine learning-based attacks that could trick and fool the surveillance systems. In addition, this thesis proposes and evaluates a lightweight defense solution called SIHFR to mitigate the impact of such adversarial-patches on CNN-based VMMR systems, leveraging the symmetry in vehicles’ face images. The experimental evaluations on recent realistic intrusion detection datasets prove the effectiveness of the developed solutions, in comparison to state-of-the-art, in detecting intrusions of various types and for different devices. Moreover, using a real-world surveillance dataset, we demonstrate the effectiveness of the SIHFR defense method which does not require re-training of the target VMMR model and adds only a minimal overhead. The solutions designed and developed in this thesis shall pave the way forward for future studies to develop efficient intrusion detection systems and adversarial attacks mitigation methods for connected surveillance systems such as VMMR.

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Foster, Mark S. "Process forensics the crossroads of checkpointing and intrusion detection /." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0008063.

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Monteiro, Valter. "How intrusion detection can improve software decoy applications." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FMonteiro.pdf.

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García, Alfaro Joaquín. "Platform of intrusion management design and implementation." Doctoral thesis, Universitat Autònoma de Barcelona, 2006. http://hdl.handle.net/10803/3053.

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Puesto que los sistemas informáticos son cada vez más vulnerables a actividades deshonestas, los mecanismos tradicionales de seguridad son todavía necesarios, pero no suficientes. Es necesario elaborar nuevos métodos de detección y de respuesta de manera que sea posible detener acciones de ataque tan pronto como sean realizadas. En esta tesis se presenta el diseño de una arquitectura de carácter general que pretende ser utilizada tanto para la realización de tareas de análisis y verificación de políticas de seguridad en red, como para controlar y configurar -sin anomalias ni errores de confguración- componentes de seguridad preventivos y de vigilancia. Se presenta también en esta tesis un mecanismo de respuesta basado en librerías de contramedidas. El objetivo de este mecanismo es ayudar al administrador a escoger posibles respuesta tan pronto como las acciones de ataque vayan siendo detectadas. Por último, se introduce también en esta tesis el diseño de una infrastructura para la comunicación entre los componentes de nuestra plataforma, y un mecanismo para la protección de dichos componentes. Todas las proposiciones y propuestas han sido implementadas y evaluadas a lo largo de nuestro trabajo. Los resultados obtenidos son presentados en las respectivas secciones de esta disertación.
Esta tesis ha sido principalmente financiada por la Agencia de Gestión y Ayudas Universitarias y de Investigación (AGAUR) del Departamento de Universidades, Investigación y Sociedad de la Información (DURSI) de la Generalitat de Catalunya (num. de referencia 2003FI00126). El trabajo ha sido conjuntamente realizado en la Universitat Autònoma de Barcelona y la Ecole Nationale Superieure des Télécommunications de Bretagne.
Palabras clave: Políticas de seguridad, detección de intrusos, contramedidas, correlación de eventos, comunicación publish/subscribe, control de acceso, protección de componentes.
Since computer infrastructures are currently getting more vulnerable than ever, traditional security mechanisms are still necessary but not suficient. We need to design effective response techniques to circumvent intrusions when they are detected. We present in this dissertation the design of a platform which is intended to act as a central point to analyze and verify network security policies, and to control and configure -without anomalies or errors- both prevention and detection security components. We also present in our work a response mechanism based on a library that implements different types of countermeasures. The objective of such a mechanism is to be a support tool in order to help the administrator to choose, in this library, the appropriate counter-measure when a given intrusion occurs. We finally present an infrastructure for the communication between the components of our platform, as well as a mechanism for the protection of such components. All these approaches and proposals have been implemented and evaluated. We present the obtained results within the respectives sections of this dissertation.
This thesis has mainly been funded by the Agency for Administration of University and Research Grants (AGAUR) of the Ministry of Education and Universities (DURSI) of the Government of Catalonia (reference number 2003FI00126). The research was jointly carried out at the Universitat Autònoma de Barcelona and at the Ecole Nationale Superieure des Télécommunications de Bretagne.
Keywords: Security policies, intrusion detection, response, counter-measures, event correlation, communication publish/subscribe, access control, components protection.

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45

Årnes, Andre. "Risk, Privacy, and Security in Computer Networks." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1725.

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With an increasingly digitally connected society comes complexity, uncertainty, and risk. Network monitoring, incident management, and digital forensics is of increasing importance with the escalation of cybercrime and other network supported serious crimes. New laws and regulations governing electronic communications, cybercrime, and data retention are being proposed, continuously requiring new methods and tools.

This thesis introduces a novel approach to real-time network risk assessment based on hidden Markov models to represent the likelihood of transitions between security states. The method measures risk as a composition of individual hosts, providing a precise, fine-grained model for assessing risk and providing decision support for incident response. The approach has been integrated with an existing framework for distributed, large-scale intrusion detection, and the results of the risk assessment are applied to prioritize the alerts produced by the intrusion detection sensors. Using this implementation, the approach is evaluated on both simulated and real-world data.

Network monitoring can encompass large networks and process enormous amounts of data, and the practice and its ubiquity can represent a great threat to the privacy and confidentiality of network users. Existing measures for anonymization and pseudonymization are analyzed with respect to the trade-off of performing meaningful data analysis while protecting the identities of the users. The results demonstrate that most existing solutions for pseudonymization are vulnerable to a range of attacks. As a solution, some remedies for strengthening the schemes are proposed, and a method for unlinkable transaction pseudonyms is considered.

Finally, a novel method for performing digital forensic reconstructions in a virtual security testbed is proposed. Based on a hypothesis of the security incident in question, the testbed is configured with the appropriate operating systems, services, and exploits. Attacks are formulated as event chains and replayed on the testbed. The effects of each event are analyzed in order to support or refute the hypothesis. The purpose of the approach is to facilitate reconstruction experiments in digital forensics. Two examples are given to demonstrate the approach; one overview example based on the Trojan defense and one detailed example of a multi-step attack. Although a reconstruction can neither prove a hypothesis with absolute certainty, nor exclude the correctness of other hypotheses, a standardized environment combined with event reconstruction and testing can lend credibility to an investigation and can be a valuable asset in court.

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La, Vinh Hoa. "Security monitoring for network protocols and applications." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL006/document.

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La sécurité informatique, aussi connue comme la cyber-sécurité, est toujours un sujet d'actualité dans la recherche en sciences informatiques. Comme les cyber-attaques grandissent de plus en plus en volume et en sophistication, la protection des systèmes ou réseaux d'information devient une tâche difficile. Les chercheurs dans la communauté de recherche prêtent une attention constante à la sécurité, en particulier ils s'orientent vers deux directions principales: (i) - la conception des infrastructures sécurisées avec des protocoles de communication sécurisés et (ii) - surveillance / supervision des systèmes ou des réseaux afin de trouver et de remédier des vulnérabilités. La dernière vérifie que tout ce qui a été conçu dans la première fonctionne correctement et en toute sécurité, ainsi détectant les violations de sécurité. Ceci étant le sujet principal de cette thèse.Cette dissertation présente un cadre de surveillance de la sécurité en tenant en compte des différents types de jeu de données d'audit y compris le trafic de réseaux et les messages échangés dans les applications. Nous proposons également des approches innovantes fondées sur l'apprentissage statistique, la théorie de l'information et de l'apprentissage automatique pour prétraiter et analyser l'entrée de données. Notre cadre est validé dans une large gamme des études de cas, y compris la surveillance des réseaux traditionnels TCP / IP (v4) (LAN, WAN, la surveillance de l'Internet), la supervision des réseaux de objets connectés utilisant la technologie 6LoWPAN (IPv6), et également, l’analyse des logs d'autres applications. Enfin, nous fournissons une étude sur la tolérance d’intrusion par conception et proposons une approche basée sur l’émulation pour détecter et tolérer l’intrusion simultanément.Dans chaque étude de cas, nous décrivons comment nous collectons les jeux de données d'audit, extrayons les attributs pertinents, traitons les données reçues et décodons leur signification de sécurité. Pour attendre ces objectifs, l'outil MMT est utilisé comme le cœur de notre approche. Nous évaluons également la performance de la solution et sa possibilité de marcher dans les systèmes “à plus grande échelle” avec des jeux de données plus volumineux
Computer security, also known as cyber-security or IT security, is always an emerging topic in computer science research. Because cyber attacks are growing in both volume and sophistication, protecting information systems or networks becomes a difficult task. Therefore, researchers in research community give an ongoing attention in security including two main directions: (i)-designing secured infrastructures with secured communication protocols and (ii)-monitoring/supervising the systems or networks in order to find and re-mediate vulnerabilities. The former assists the later by forming some additional monitoring-supporting modules. Whilst, the later verifies whether everything designed in the former is correctly and securely functioning as well as detecting security violations. This is the main topic of this thesis.This dissertation presents a security monitoring framework that takes into consideration different types of audit dataset including network traffic and application logs. We propose also some novel approaches based on supervised machine learning to pre-process and analyze the data input. Our framework is validated in a wide range of case studies including traditional TCP/IPv4 network monitoring (LAN, WAN, Internet monitoring), IoT/WSN using 6LoWPAN technology (IPv6), and other applications' logs. Last but not least, we provide a study regarding intrusion tolerance by design and propose an emulation-based approach to simultaneously detect and tolerate intrusion.In each case study, we describe how we collect the audit dataset, extract the relevant attributes, handle received data and decode their security meaning. For these goals, the tool Montimage Monitoring Tool (MMT) is used as the core of our approach. We assess also the solution's performance and its possibility to work in "larger scale" systems with more voluminous dataset

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47

Gu, Guofei. "Correlation-based Botnet Detection in Enterprise Networks." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24634.

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Most of the attacks and fraudulent activities on the Internet are carried out by malware. In particular, botnets, as state-of-the-art malware, are now considered as the largest threat to Internet security. In this thesis, we focus on addressing the botnet detection problem in an enterprise-like network environment. We present a comprehensive correlation-based framework for multi-perspective botnet detection consisting of detection technologies demonstrated in four complementary systems: BotHunter, BotSniffer, BotMiner, and BotProbe. The common thread of these systems is correlation analysis, i.e., vertical correlation (dialog correlation), horizontal correlation, and cause-effect correlation. All these Bot* systems have been evaluated in live networks and/or real-world network traces. The evaluation results show that they can accurately detect real-world botnets for their desired detection purposes with a very low false positive rate. We find that correlation analysis techniques are of particular value for detecting advanced malware such as botnets. Dialog correlation can be effective as long as malware infections need multiple stages. Horizontal correlation can be effective as long as malware tends to be distributed and coordinated. In addition, active techniques can greatly complement passive approaches, if carefully used. We believe our experience and lessons are of great benefit to future malware detection.

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48

Judd, John David. "Stream splitting in support of intrusion detection." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FJudd.pdf.

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49

Kalibjian, Jeffrey R. "APPLICATION OF INTRUSION DETECTION SOFTWARE TO PROTECT TELEMETRY DATA IN OPEN NETWORKED COMPUTER ENVIRONMENTS." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/606817.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California
Over the past few years models for Internet based sharing and selling of telemetry data have been presented [1] [2] [3] at ITC conferences. A key element of these sharing/selling architectures was security. This element was needed to insure that information was not compromised while in transit or to insure particular parties had a legitimate right to access the telemetry data. While the software managing the telemetry data needs to be security conscious, the networked computer hosting the telemetry data to be shared or sold also needs to be resistant to compromise. Intrusion Detection Systems (IDS) may be used to help identify and protect computers from malicious attacks in which data can be compromised.

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Zomlot, Loai M. M. "Handling uncertainty in intrusion analysis." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/17603.

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Doctor of Philosophy
Department of Computing and Information Sciences
Xinming Ou
Intrusion analysis, i.e., the process of combing through Intrusion Detection System (IDS) alerts and audit logs to identify true successful and attempted attacks, remains a difficult problem in practical network security defense. The primary cause of this problem is the high false positive rate in IDS system sensors used to detect malicious activity. This high false positive rate is attributed to an inability to differentiate nearly certain attacks from those that are merely possible. This inefficacy has created high uncertainty in intrusion analysis and consequently causing an overwhelming amount of work for security analysts. As a solution, practitioners typically resort to a specific IDS-rules set that precisely captures specific attacks. However, this results in failure to discern other forms of the targeted attack because an attack’s polymorphism reflects human intelligence. Alternatively, the addition of generic rules so that an activity with remote indication of an attack will trigger an alert, requires the security analyst to discern true alerts from a multitude of false alerts, thus perpetuating the original problem. The perpetuity of this trade-off issue is a dilemma that has puzzled the cyber-security community for years. A solution to this dilemma includes reducing uncertainty in intrusion analysis by making IDS-nearly-certain alerts prominently discernible. Therefore, I propose alerts prioritization, which can be attained by integrating multiple methods. I use IDS alerts correlation by building attack scenarios in a ground-up manner. In addition, I use Dempster-Shafer Theory (DST), a non-traditional theory to quantify uncertainty, and I propose a new method for fusing non-independent alerts in an attack scenario. Finally, I propose usage of semi-supervised learning to capture an organization’s contextual knowledge, consequently improving prioritization. Evaluation of these approaches was conducted using multiple datasets. Evaluation results strongly indicate that the ranking provided by the approaches gives good prioritization of IDS alerts based on their likelihood of indicating true attacks.

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Dissertations / Theses on the topic 'Network security intrusion detection'

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