Relevant bibliographies by topics / Adaptive Intrusion Detection System

Academic literature on the topic 'Adaptive Intrusion Detection System'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Adaptive Intrusion Detection System"

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A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system.
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Simavoryan, Simon Zhorzhevich, Arsen Rafikovich Simonyan, Georgii Aleksandrovich Popov, and Elena Ivanovna Ulitina. "The procedure of intrusions detection in information security systems based on the use of neural networks." Программные системы и вычислительные методы, no. 3 (March 2020): 1–9. http://dx.doi.org/10.7256/2454-0714.2020.3.33734.

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The subject of the research is the problem of identifying and countering intrusions (attacks) in information security systems (ISS) based on the system-conceptual approach, developed within the framework of the RFBR funded project No. 19-01-00383. The object of the research is neural networks and information security systems (ISS) of automated data processing systems (ADPS). The authors proceed from the basic conceptual requirements for intrusion detection systems - adaptability, learnability and manageability. The developed intrusion detection procedure considers both internal and external threats. It consists of two subsystems: a subsystem for detecting possible intrusions, which includes subsystems for predicting, controlling and managing access, analyzing and detecting the recurrence of intrusions, as well as a subsystem for countering intrusions, which includes subsystems for blocking / destroying protected resources, assessing losses associated with intrusions, and eliminating the consequences of the invasion. Methodological studies on the development of intrusion detection procedures are carried out using artificial intelligence methods, system analysis, and the theory of neural systems in the field of information security. Research in this work is carried out on the basis of the achievements of the system-conceptual approach to information security in ADPS.The main result obtained in this work is a block diagram (algorithm) of an adaptive intrusion detection procedure, which contains protection means and mechanisms, built by analogy with neural systems used in security systems.The developed general structure of the intrusion detection and counteraction system allows systematically interconnecting the subsystems for detecting possible intrusions and counteracting intrusions at the conceptual level.
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Liu, Yang Bin, Liang Shi, Bei Zhan Wang, Yuan Qin Wu, and Pan Hong Wang. "An New Agent Based Distributed Adaptive Intrusion Detection System." Advanced Materials Research 532-533 (June 2012): 624–29. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.624.

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In order to overcome the excessive dependence among the traditional intrusion detection system components, high rate false-alarm phenomenon caused by multiple alarms to the same invasion, inability to adaptively replace mining algorithm when testing environment has changed and other issues, this paper puts forward an Agent based distributed adaptive intrusion detection system, which employs Joint Detection mechanism for mining algorithm module, and Dynamic Election algorithm for the recovery mechanism, thereby improving the system adaptive ability to the external change.
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Yu, Zhenwei, Jeffrey J. P. Tsai, and Thomas Weigert. "An adaptive automatically tuning intrusion detection system." ACM Transactions on Autonomous and Adaptive Systems 3, no. 3 (August 2008): 1–25. http://dx.doi.org/10.1145/1380422.1380425.

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P.S., Pawar, and Hashmi S.A. "Security Enhanced Adaptive Acknowledgment Intrusion Detection System." International Journal of Computer Applications 130, no. 7 (November 17, 2015): 51–56. http://dx.doi.org/10.5120/ijca2015907055.

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Elfeshawy, Nawal A., and Osama S. Faragallah. "Divided two-part adaptive intrusion detection system." Wireless Networks 19, no. 3 (June 13, 2012): 301–21. http://dx.doi.org/10.1007/s11276-012-0467-7.

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Ibrahim, Nurudeen Mahmud, and Anazida Zainal. "An Adaptive Intrusion Detection Scheme for Cloud Computing." International Journal of Swarm Intelligence Research 10, no. 4 (October 2019): 53–70. http://dx.doi.org/10.4018/ijsir.2019100104.

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To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.
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Hacini, Salima, Zahia Guessoum, and Mohamed Cheikh. "False Alarm Reduction Using Adaptive Agent-Based Profiling." International Journal of Information Security and Privacy 7, no. 4 (October 2013): 53–74. http://dx.doi.org/10.4018/ijisp.2013100105.

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In this paper the authors propose a new efficient anomaly-based intrusion detection mechanism based on multi-agent systems. New networks are particularly vulnerable to intrusion, they are often attacked with intelligent and skilful hacking techniques. The intrusion detection techniques have to deal with two problems: intrusion detection and false alarms. The issue of false alarms has an important impact on the success of the anomaly-based intrusion detection technologies. The purpose of this paper is to improve their accuracy by detecting real attacks and by reducing the number of unnecessary generated alerts. The authors' intrusion detection mechanism relies on a set of agents to ensure the detection and the adaptation of normal profile to support the legitimate dynamic changes that occur and are the cause of high rate of false alarms.
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Chiche, Alebachew, and Million Meshesha. "Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection." Journal of Computer Networks and Communications 2021 (January 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/8845540.

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This paper introduces a new integrated learning approach towards developing a new network intrusion detection model that is scalable and adaptive nature of learning. The approach can improve the existing trends and difficulties in intrusion detection. An integrated approach of machine learning with knowledge-based system is proposed for intrusion detection. While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. It is empirically tested with NSL-KDD dataset of 40,558 total instances, by using ten-fold cross validation. Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. Therefore, security experts are recommended to integrate intrusion detection in their network and computer systems, not only for well-being of their computer systems but also for the sake of improving their working process.
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Owens, Stephen F., and Reuven R. Levary. "An adaptive expert system approach for intrusion detection." International Journal of Security and Networks 1, no. 3/4 (2006): 206. http://dx.doi.org/10.1504/ijsn.2006.011780.

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Dissertations / Theses on the topic "Adaptive Intrusion Detection System"

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Barrios, Rita M. "An Adaptive Database Intrusion Detection System." NSUWorks, 2011. http://nsuworks.nova.edu/gscis_etd/86.

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Intrusion detection is difficult to accomplish when attempting to employ current methodologies when considering the database and the authorized entity. It is a common understanding that current methodologies focus on the network architecture rather than the database, which is not an adequate solution when considering the insider threat. Recent findings suggest that many have attempted to address this concern with the utilization of various detection methodologies in the areas of database authorization, security policy management and behavior analysis but have not been able to find an adequate solution to achieve the level of detection that is required. While each of these methodologies has been addressed on an individual basis, there has been very limited work to address the methodologies as a single entity in an attempt to function within the detection environment in a harmonious fashion. Authorization is at the heart of most database implementations however, is not enough to prevent a rogue, authorized entity from instantiating a malicious action. Similarly, eliminating the current security policies only exacerbates the problem due to a lack of knowledge in a fashion when the policies have been modified. The behavior of the authorized entity is the most significant concern in terms of intrusion detection. However, behavior identification methodologies alone will not produce a complete solution. The detection of the insider threat during database access by merging the individual intrusion detection methodologies as noted will be investigated. To achieve the goal, this research is proposing the creation of a procedural framework to be implemented as a precursor to the effecting of the data retrieval statement. The intrusion model and probability thresholds will be built utilizing the intrusion detection standards as put forth in research and industry. Once an intrusion has been indicated, the appropriate notifications will be distributed for further action by the security administrator while the transaction will continue to completion. This research is proposing the development of a Database Intrusion Detection framework with the introduction of a process as defined in this research, to be implemented prior to data retrieval. This addition will enable an effective and robust methodology to determine the probability of an intrusion by the authorized entity, which will ultimately address the insider threat phenomena.
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Stakhanova, Natalia. "A framework for adaptive, cost-sensitive intrusion detection and response system." [Ames, Iowa : Iowa State University], 2007.

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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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Shafi, Kamran Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "An online and adaptive signature-based approach for intrusion detection using learning classifier systems." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38991.

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This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, which are referred to as signatures, from network traffic through a supervised learning classifier system, UCS. The rules are learnt dynamically (i.e., using machine intelligence and without the requirement of a domain expert), and adaptively (i.e., as the data arrives without the need to relearn the complete model after presenting each data instance to the current model). Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt. The rule based profiling of normal behaviour allows for anomaly detection in that the events not matching any of the rules are considered potentially harmful and could be escalated for an action. We study the effect of key UCS parameters and operators on its performance and identify areas of improvement through this analysis. Several new heuristics are proposed that improve the effectiveness of UCS for the prediction of unseen and extremely rare intrusive activities. A signature extraction system is developed that adaptively retrieves signatures as they are discovered by UCS. The signature extraction algorithm is augmented by introducing novel subsumption operators that minimise overlap between signatures. Mechanisms are provided to adapt the main algorithm parameters to deal with online noisy and imbalanced class data. The performance of UCS, its variants and the signature extraction system is measured through standard evaluation metrics on a publicly available intrusion detection dataset provided during the 1999 KDD Cup intrusion detection competition. We show that the extended UCS significantly improves test accuracy and hit rate while significantly reducing the rate of false alarms and cost per example scores than the standard UCS. The results are competitive to the best systems participated in the competition in addition to our systems being online and incremental rule learners. The signature extraction system built on top of the extended UCS retrieves a magnitude smaller rule set than the base UCS learner without any significant performance loss. We extend the evaluation of our systems to real time network traffic which is captured from a university departmental server. A methodology is developed to build fully labelled intrusion detection dataset by mixing real background traffic with attacks simulated in a controlled environment. Tools are developed to pre-process the raw network data into feature vector format suitable for UCS and other related machine learning systems. We show the effectiveness of our feature set in detecting payload based attacks.
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Mohan, Sujaa Rani Park E. K. "Association rule based data mining approaches for Web Cache Maintenance and adaptive Intrusion Detection systems." Diss., UMK access, 2005.

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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2005.
"A thesis in computer science." Typescript. Advisor: E.K. Park. Vita. Title from "catalog record" of the print edition Description based on contents viewed March 12, 2007. Includes bibliographical references (leaves 159-162). Online version of the print edition.
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Grizzard, Julian B. "Towards Self-Healing Systems: Re-establishing Trust in Compromised Systems." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-04072006-133056/.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006.
Schwan, Karsten, Committee Member ; Schimmel, David, Committee Member ; Copeland, John, Committee Member ; Owen, Henry, Committee Chair ; Wills, Linda, Committee Member.
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Sargolzaei, Arman. "Time-Delay Switch Attack on Networked Control Systems, Effects and Countermeasures." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2175.

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In recent years, the security of networked control systems (NCSs) has been an important challenge for many researchers. Although the security schemes for networked control systems have advanced in the past several years, there have been many acknowledged cyber attacks. As a result, this dissertation proposes the use of a novel time-delay switch (TDS) attack by introducing time delays into the dynamics of NCSs. Such an attack has devastating effects on NCSs if prevention techniques and countermeasures are not considered in the design of these systems. To overcome the stability issue caused by TDS attacks, this dissertation proposes a new detector to track TDS attacks in real time. This method relies on an estimator that will estimate and track time delays introduced by a hacker. Once a detector obtains the maximum tolerable time delay of a plant’s optimal controller (for which the plant remains secure and stable), it issues an alarm signal and directs the system to its alarm state. In the alarm state, the plant operates under the control of an emergency controller that can be local or networked to the plant and remains in this stable mode until the networked control system state is restored. In another effort, this dissertation evaluates different control methods to find out which one is more stable when under a TDS attack than others. Also, a novel, simple and effective controller is proposed to thwart TDS attacks on the sensing loop (SL). The modified controller controls the system under a TDS attack. Also, the time-delay estimator will track time delays introduced by a hacker using a modified model reference-based control with an indirect supervisor and a modified least mean square (LMS) minimization technique. Furthermore, here, the demonstration proves that the cryptographic solutions are ineffective in the recovery from TDS attacks. A cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is introduced to leverage the adaptive channel redundancy techniques, along with a novel state estimator to detect and assist in the recovery of the destabilizing effects of TDS attacks. The conclusion shows how the CF-TDSR ensures the control stability of linear time invariant systems.
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Sainani, Varsha. "Hybrid Layered Intrusion Detection System." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/44.

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The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intrusion detection system is created, particularly with the support of a multi-class supervised classification technique. In agent-based IDS, there is no central control and therefore no central point of failure. Agents can detect and take predefined actions against malicious activities, which can be detected with the help of data mining techniques. The proposed IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDSs with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on a multiagent platform along with a supervised classification technique. Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. To facilitate information exchange between different agents in the proposed hybrid layered multiagent architecture, a low cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that this system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time on the network.
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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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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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Books on the topic "Adaptive Intrusion Detection System"

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Tsukerman, Emmanuel. Designing a Machine Learning Intrusion Detection System. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6591-8.

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Herrero, Alvaro. Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Real world Linux security: Intrusion protection, detection, and recovery. 2nd ed. Upper Saddle River, N.J: Prentice Hall, 2003.

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Toxen, Bob. Real-world Linux security: Intrusion, prevention, detection, and recovery. Upper Saddle River, NJ: Prentice Hall, 2001.

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Real-world Linux security: Intrusion, prevention, detection, and recovery. Upper Saddle River, NJ: Prentice Hall, 2001.

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Spenneberg, Ralf. Intrusion Detection fu r Linux-Server: Mit Open-Source-Tools Angriffe erkennen und analysieren ; mit einer Einfu hrung in die digitale Forensik. Mu nchen/Germany: Markt-und-Technik-Verl., 2003.

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Carter, Earl. Cisco Secure Intrusion Detection System. Cisco Press, 2001.

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Corchado, Emilio, and Álvaro Herrero. Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System. Springer, 2014.

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Blokdyk, Gerardus. Intrusion Detection System a Complete Guide - 2020 Edition. Emereo Pty Limited, 2020.

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Intrusion Detection System Evasion durch Angriffsverschleierung in Exploiting Frameworks. Thomas Stein, 2010.

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Book chapters on the topic "Adaptive Intrusion Detection System"

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Stiborek, Jan, Martin Grill, Martin Rehak, Karel Bartos, and Jan Jusko. "Game Theoretical Model for Adaptive Intrusion Detection System." In Transactions on Computational Collective Intelligence XV, 133–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45910-2_7.

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Stiborek, Jan, Martin Grill, Martin Rehak, Karel Bartos, and Jan Jusko. "Game Theoretical Model for Adaptive Intrusion Detection System." In Transactions on Computational Collective Intelligence XV, 133–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44750-5_7.

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Tiwari, Purnesh, Prakash Mishra, Upendra Singh, and Ravikant Itare. "New Adaptive Resonance Theory Based Intrusion Detection System." In Second International Conference on Computer Networks and Communication Technologies, 745–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37051-0_84.

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Lee, Ki-Hyun, and Young B. Park. "A Study of Environment-Adaptive Intrusion Detection System." In Advances in Computer Science and Ubiquitous Computing, 625–30. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3023-9_96.

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Lee, Chang Seok, and Sung Bae Cho. "Learning Classifier Systems for Adaptive Learning of Intrusion Detection System." In International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding, 557–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67180-2_54.

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Lu, Qiuwen, Zhou Zhou, Hongzhou Sha, Qingyun Liu, and Hongcheng Sun. "Self-Adaptive Frequency Scaling Architecture for Intrusion Detection System." In Trustworthy Computing and Services, 74–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47401-3_10.

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Dixit, Mrudul, and Rajashwini Ukarande. "Internet Traffic Intrusion Detection System Using Adaptive Neuro-Fuzzy Inference System." In Communications in Computer and Information Science, 21–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1423-0_3.

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Mulimani, Deepa, Shashikumar G. Totad, Prakashgoud Patil, and Shivananda V. Seeri. "Adaptive Ensemble Learning with Concept Drift Detection for Intrusion Detection." In Advances in Intelligent Systems and Computing, 331–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0171-2_31.

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Krishnan, Deepa, and Madhumita Chatterjee. "An Adaptive Distributed Intrusion Detection System for Cloud Computing Framework." In Communications in Computer and Information Science, 466–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34135-9_45.

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Sethi, Kamalakanta, Rahul Kumar, Dinesh Mohanty, and Padmalochan Bera. "Robust Adaptive Cloud Intrusion Detection System Using Advanced Deep Reinforcement Learning." In Security, Privacy, and Applied Cryptography Engineering, 66–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66626-2_4.

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Conference papers on the topic "Adaptive Intrusion Detection System"

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Satam, Pratik. "Cross Layer Anomaly Based Intrusion Detection System." In 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW). IEEE, 2015. http://dx.doi.org/10.1109/sasow.2015.31.

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Nguyen, Hai Thanh, and Katrin Franke. "Adaptive Intrusion Detection System via online machine learning." In 2012 12th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2012. http://dx.doi.org/10.1109/his.2012.6421346.

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Zhao, Lin-hui, Yumin Wang, Jing Xiao, Ya-ping Dai, Fang-yan Dong, and Hai-le Liu. "A Three-Level-Module Adaptive Intrusion Detection System." In 2007 IEEE International Conference on Networking, Sensing and Control. IEEE, 2007. http://dx.doi.org/10.1109/icnsc.2007.372890.

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Jiancheng, NI, LI Zhishu, SUN Jirong, and XING Jianchuan. "Self-adaptive Intrusion Detection System for Computational Grid." In First Joint IEEE/IFIP Symposium on Theoretical Aspects of Software Engineering (TASE '07). IEEE, 2007. http://dx.doi.org/10.1109/tase.2007.44.

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Novokhodko, Alexander, Donald C. Wunsch II, and Cihan H. Dagli. "Adaptive critic design for computer intrusion detection system." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Kevin L. Priddy, Paul E. Keller, and Peter J. Angeline. SPIE, 2001. http://dx.doi.org/10.1117/12.421156.

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Chavan, S., K. Shah, N. Dave, S. Mukherjee, A. Abraham, and S. Sanyal. "Adaptive neuro-fuzzy intrusion detection systems." In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. IEEE, 2004. http://dx.doi.org/10.1109/itcc.2004.1286428.

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Kim, SunWoo, TaeGuen Kim, and Eul Gyu Im. "Real-time malware detection framework in intrusion detection systems." In the 2013 Research in Adaptive and Convergent Systems. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2513228.2513297.

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Deng, Lei, and De-yuan Gao. "Research on Immune Based Adaptive Intrusion Detection System Model." In 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC). IEEE, 2009. http://dx.doi.org/10.1109/nswctc.2009.87.

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Rangadurai Karthick, R., Vipul P. Hattiwale, and Balaraman Ravindran. "Adaptive network intrusion detection system using a hybrid approach." In 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2012. http://dx.doi.org/10.1109/comsnets.2012.6151345.

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Chen, Shan-Tai, Chih-Chen Pan, and Chyi-Bao Yang. "An Adaptive Feedback Mechanism Algorithm for Intrusion Detection System." In 2009 2nd International Conference on Computer Science and its Applications (CSA). IEEE, 2009. http://dx.doi.org/10.1109/csa.2009.5404260.

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Reports on the topic "Adaptive Intrusion Detection System"

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Lundy, Philip A., George W. Pittman, and Heinz J. Pletsch. Intrusion Detection System Methodology Investigation. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada198210.

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DRAELOS, TIMOTHY J., MICHAEL J. COLLINS, DAVID P. DUGGAN, EDWARD V. THOMAS, and DONALD WUNSCH. Experiments on Adaptive Techniques for Host-Based Intrusion Detection. Office of Scientific and Technical Information (OSTI), September 2001. http://dx.doi.org/10.2172/787645.

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Zage, Dolores M., and Wayne M. Zage. Intrusion Detection System Visualization of Network Alerts. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada532723.

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Speed, Ann. Intrusion Detection System Alarm Station Operator Interface Improvements. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1762330.

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Heady, R., G. Luger, A. Maccabe, and M. Servilla. The architecture of a network level intrusion detection system. Office of Scientific and Technical Information (OSTI), August 1990. http://dx.doi.org/10.2172/425295.

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Skormin, Victor A. Anomaly-Based Intrusion Detection Systems Utilizing System Call Data. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada568124.

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Chen, Yan. HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1108982.

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Kent, Stephen T., and Luis A. Sanchez. Secure Border Gateway Protocol and the External Routing Intrusion Detection System. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada386679.

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Kemmer, Richard A., and Giovanni Vigna. A Model-Based Real-Time Intrusion Detection System for Large Scale Heterogeneous Networks. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada420824.

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Fink, G. A., B. L. Chappell, T. G. Turner, and K. F. O'Donoghue. A Metrics-Based Approach to Intrusion Detection System Evaluation for Distributed Real-Time Systems. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada406577.

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