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Academic literature on the topic 'Attack on the network'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Attack on the network"

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Ahmed, Abdulghani Ali. "Investigation Approach for Network Attack Intention Recognition." International Journal of Digital Crime and Forensics 9, no. 1 (January 2017): 17–38. http://dx.doi.org/10.4018/ijdcf.2017010102.

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Sensitive information has critical risks when transmitted through computer networks. Existing protection systems still have limitations with treating network information with sufficient confidentiality, integrity, and availability. The rapid development of network technologies helps increase network attacks and hides their malicious intentions. Attack intention is the ultimate attack goal that the attacker attempts to achieve by executing various intrusion methods or techniques. Recognizing attack intentions helps security administrator develop effective protection systems that can detect network attacks that have similar intentions. This paper analyses attack types and classifies them according to their malicious intent. An investigation approach based on similarity metric is proposed to recognize attacker plans and predict their intentions. The obtained results demonstrate that the proposed approach is capable of investigating similarity of attack signatures and recognizing the intentions of Network attack.
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Singhal, Prateek, Puneet Sharma, and Deepak Arora. "An approach towards preventing iot based sybil attack based on contiki framework through cooja simulator." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 261. http://dx.doi.org/10.14419/ijet.v7i2.8.10421.

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In this paper we propagate the Sybil attack in WSN (Wireless sensor network), by the researchers many attacks have been recognized in WSN till now and there are many attacks which can attack over through internet, Internet of thing means all devices is interconnected to each other M2M over internet and can be attacked by any of the attacker on any devices. Sybil attack is the detrimental attack against sensor network where several counterfeit identities and legitimate identities are used to get prohibited pass in a network. This is major attack which results an information loss and misinterpretation in the network, and it also minimizes the routing disturbance, trustworthiness and dropping sensitivity packets into a network. In this instance node can trust the imaginary node and sharing of information starts, owed to this security of node is get affected and information is lost. In this paper, a survey of CONTIKI OS-2.7, stimulation tool COOJA and the Sybil attack and proposed the defense mechanisms and CAM (Compare and Match) approach to verify the Sybil attack position and prevent it. This Sybil attack can be stimulated on the stimulation tool COOJA which helps to identify the attacker position node, whereas these attacks outcome in uni-casting as well as multicasting and in this paper specifically given the secure security for Wireless sensor network.
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Glăvan, Dragoș. "Sniffing attacks on computer networks." Scientific Bulletin of Naval Academy XXIII, no. 1 (July 15, 2020): 202–7. http://dx.doi.org/10.21279/1454-864x-20-i1-027.

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The sniffing attack or sniffer attack, in the context of network security, corresponds to data theft or interception by capturing network traffic using a sniffer (an application that aims to capture network packets). When data is transmitted over networks, if data packets are not encrypted, data in the network packet can be read using a sniffer. Using a sniffer application, an attacker can analyze the network and obtain information so that it can eventually crash or corrupt the network or read the communications that occur in the network. Sniffing attacks can be compared to touching wires and getting to know the conversation, and for this reason it is also called "wiretapping" applied to computer networks. In this paper, a sniffing attack is shared which can significantly damage the computer networks as well as methods of combating such attacks. Sniffing is usually performed to analyze network usage, troubleshoot network problems, monitor session for development and testing purposes.
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Winda Andrini Wulandari. "THE ANALYSIS NETWORK FORENSICS USING HONEYPOT ON PUBLIC CLOUD COMPUTING SERVICE NETWORK." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 3, no. 1 (April 20, 2018): 18–25. http://dx.doi.org/10.20527/jtiulm.v3i1.24.

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This research presents visualization in dashboard using AWN campus honeypot log data connected to IHP (Kemkominfo) Jakarta on public cloud computing service network to categorize time stamp in data. Package attack data is divided into three categories namely morning, noon, and night based on Time Western Indonesia (WIB). DDoS attacks attacked several ports 21, 80, 135, and 445. K -means clustering method is implemented in this research to get categorization result of time of effective attack to know DDoS attack attack and cyber profilling which is expected to help monitoring process of anticipation of vulnerability cloud network of ddos / cyber crime attacks. The results of this study indicate that the method used to obtain results in accordance with the objectives.
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Kamaleshwar, T., R. Lakshminarayanan, Yuvaraja Teekaraman, Ramya Kuppusamy, and Arun Radhakrishnan. "Self-Adaptive Framework for Rectification and Detection of Black Hole and Wormhole Attacks in 6LoWPAN." Wireless Communications and Mobile Computing 2021 (December 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/5143124.

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Internet network communication protocol version 6 low-power wireless personal area networks (6LoWPAN) is supposed to assist the gadgets with low-power wireless sensor network (WSN) and it furnishes the top model layer of the data transmission system. The 6LoWPAN is prone to the diversified attacks such as wormhole and black hole attacks, which might be very difficult to become aware of and defend. In a wormhole attack, the attacker listens to the facts over the networks, and in a black hole attack, the intruder reprograms the nodes to dam the data transmission. As an end result, any data datagram that enters the attacked region will end result in transmission failure with low flow network rate and excessive one-way delay. To come across and heal the attack, a self-adaptive framework is brought into the networks and the procedure of data transmission is enriched. In this work, the affected region is measured and rectified with the aid of using the proposed self-adaptive framework for Ad Hoc On-Demand Distance Vector (AODV) routing protocol network communication protocol. The overall performance of the network healing technique is investigated with the aid of using simulation and its miles diagnosed that the proposed framework suggests promising overall performance with the aid of using accomplishing excessive flow network rate and minimum delay.
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Park, Sanglee, and Jungmin So. "On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification." Applied Sciences 10, no. 22 (November 14, 2020): 8079. http://dx.doi.org/10.3390/app10228079.

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State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks. Throughout the efforts to make the models robust against adversarial example attacks, it has been found to be a very difficult task. While many defense approaches were shown to be not effective, adversarial training remains as one of the promising methods. In adversarial training, the training data are augmented by “adversarial” samples generated using an attack algorithm. If the attacker uses a similar attack algorithm to generate adversarial examples, the adversarially trained network can be quite robust to the attack. However, there are numerous ways of creating adversarial examples, and the defender does not know what algorithm the attacker may use. A natural question is: Can we use adversarial training to train a model robust to multiple types of attack? Previous work have shown that, when a network is trained with adversarial examples generated from multiple attack methods, the network is still vulnerable to white-box attacks where the attacker has complete access to the model parameters. In this paper, we study this question in the context of black-box attacks, which can be a more realistic assumption for practical applications. Experiments with the MNIST dataset show that adversarially training a network with an attack method helps defending against that particular attack method, but has limited effect for other attack methods. In addition, even if the defender trains a network with multiple types of adversarial examples and the attacker attacks with one of the methods, the network could lose accuracy to the attack if the attacker uses a different data augmentation strategy on the target network. These results show that it is very difficult to make a robust network using adversarial training, even for black-box settings where the attacker has restricted information on the target network.
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Qin, Yuan. "Computer Network Attack Modeling and Network Attack Graph Study." Advanced Materials Research 1079-1080 (December 2014): 816–19. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.816.

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With the development of computer network and rapid popularity of Internet, network information security has become the focus of safeguarding national security and social stability. In the network security event, the hacker often can’t successfully intrude into the network by means of a single host / services hacker. With the help of various kinds of "vulnerability" generated bydifferent relationship existing in multiple point multiple host, the hacker can achieve the purpose of network intrusion. Therefore one important aspect of network security is after obtaining the vulnerability of the network information, considering a combination of multiple exploits and analyzing the attack path of network penetration attacks that the attacker may take.
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Gandhi, Kaushal, Rajneesh Narula, Sumeer Khullar, and Anish Arora. "Security Issues of Routing Protocols in MANETs." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (October 30, 2012): 339–42. http://dx.doi.org/10.24297/ijct.v3i2c.2894.

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There are a number of routing protocols developed by researchers. Due to the nature of ad hoc networks, secure routing is an important area of research in developing secure routing protocols. Although researchers have proposed several secure routing protocols, their resistance towards various types of security attacks and efficiency are primary points of concern in implementing these protocols. This paper presents some of the available secure routing protocols and most common attack patterns against ad hoc networks. Routing protocols are subjected to case studies against the most commonly identified attack patterns such as: denial-of-service attack, tunneling, spoofing, black hole attack and wormhole attack etc. In MANET, the nodes also function as routers that discover and maintain routes to other nodes in the network. Establishing an optimal and efficient route between the communicating parties is the primary concern of the routing protocols of MANET. Any attack in routing phase may disrupt the overall communication and the entire network can be paralyzed. Thus, security in network layer plays an important role in the security of the whole network. A number of attacks in network layer have been identified and studied in security research. An attacker can absorb network traffic, inject themselves into the path between the source and destination and thus control the network traffic flow.
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Sehrawat, Harkesh, Yudhvir Singh, and Vikas Siwach. "Analysis of AODV protocol under sinkhole attack in wireless sensor network." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 153. http://dx.doi.org/10.14419/ijet.v7i2.4.13028.

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A Wireless Sensor Network (WSNs) is a collection of number of sensor nodes which are left open in an unsecured environment. Sensor nodes work and communicate together to attain the desired goals. They are placed at the locations where monitoring is otherwise impossible. Wireless Sensor Networks are resource constrained which may be computational power, memory capacity, battery power etc. As Wireless Sensor Networks are implemented in the unattended environment, they are prone to discrete type of security attacks. Because of their limitations these networks are easily targeted by intruders. Sinkhole attack is one of the security attacks which try to disturb the ongoing communication in wireless sensor network. In sinkhole attack, the intruder or the malicious node try to attract the network traffic towards itself, that sensor nodes will pass data packets through this compromised node thereby manipulating messages which sensor nodes are transferring to the base station. In this paper we analyze the impact of Sinkhole attack on AODV protocol under various conditions. We analyzed the impact of Sinkhole attack on AODV protocol with varying number of attacker nodes.
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Moskal, Stephen, Shanchieh Jay Yang, and Michael E. Kuhl. "Cyber threat assessment via attack scenario simulation using an integrated adversary and network modeling approach." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 15, no. 1 (August 15, 2017): 13–29. http://dx.doi.org/10.1177/1548512917725408.

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Existing research on cyber threat assessment focuses on analyzing the network vulnerabilities and producing possible attack graphs. Cyber attacks in real-world enterprise networks, however, vary significantly due to not only network and system configurations, but also the attacker’s strategies. This work proposes a cyber-based attacker behavior model (ABM) in conjunction with the Cyber Attack Scenario and Network Defense Simulator to model the interaction between the network and the attackers. The ABM leverages a knowledge-based design and factors in the capability, opportunity, intent, preference, and Cyber Attack Kill Chain integration to model various types of attackers. By varying the types of attackers and the network configurations, and simulating their interactions, we present a method to measure the overall network security against cyber attackers under different scenarios. Simulation results based on four attacker types on two network configurations are shown to demonstrate how different attacker behaviors may lead to different ways to penetrate a network, and how a single misconfiguration may impact network security.
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Dissertations / Theses on the topic "Attack on the network"

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Avidan, Lenoy. "Dynamic Shifting of Virtual Network Topologies for Network Attack Prevention." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/1986.

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Computer networks were not designed with security in mind, making research into the subject of network security vital. Virtual Networks are similar to computer networks, except the components of a Virtual Network are in software rather than hardware. With the constant threat of attacks on networks, security is always a big concern, and Virtual Networks are no different. Virtual Networks have many potential attack vectors similar to physical networks, making research into Virtual Network security of great importance. Virtual Networks, since they are composed of virtualized network components, have the ability to dynamically change topologies. In this paper, we explore Virtual Networks and their ability to quickly shift their network topology. We investigate the potential use of this flexibility to protect network resources and defend against malicious activities. To show the ability of reactively shifting a Virtual Network’s topology to se- cure a network, we create a set of four experiments, each with a different dynamic topology shift, or “dynamic defense”. These four groups of experiments are called the Server Protection, Isolated Subnet, Distributed Port Group, and Standard Port Group experiments. The Server Protection experiments involve detecting an attack against a server and shifting the server behind a protected subnet. The other three sets of experiments, called Attacker Prevention experiments, involve detecting a malicious node in the internal network and initiating a dynamic de- fense to move the attacker behind a protected subnet. Each Attacker Prevention experiment utilizes a different dynamic defense to prevent the malicious node from attacking the rest of the Virtual Network. For each experiment, we run 6 different network attacks to validate the effectiveness of the dynamic defenses. The network attacks utilized for each experiment are ICMP Flooding, TCP Syn Flooding, Smurf attack, ARP Spoofing, DNS Spoofing, and NMAP Scanning. Our validation shows that our dynamic defenses, outside of the standard port group, are very effective in stopping each attack, consistently lowering the at- tacks’ success rate significantly. The Standard Port Group was the one dynamic defense that is ineffective, though there are also a couple of experiments that could benefit from being run with more attackers and with different situations to fully understand the effectiveness of the defenses. We believe that, as Virtual Networks become more common and utilized outside of data centers, the ability to dynamically shift topology can be used for network security purposes.
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Karaaslan, Ibrahim. "Anti-sensor Network: Distortion-based Distributed Attack In Wireless Sensor Networks." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609276/index.pdf.

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In this thesis, a novel anti-sensor network paradigm is introduced against wireless sensor networks (WSN). Anti-sensor network (ASN) aims to destroy application reliability by adaptively and anonymously introducing adequate level of artificial distortion into the communication of the event features transported from the sensor nodes (SN) to the sink. ASN is composed of anti-sensor nodes (aSN) randomly distributed over the sensor network field. aSNs pretend to be SNs tomaintain anonymity and so improve resiliency against attack detection and prevention mechanisms. Performance evaluations via mathematical analysis and simulation experiments show that ASN can effectively reduce the application reliability of WSN.
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Gerbert, Oscar. "Attack on the Chaos Sensor Network Protocol." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260480.

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As the demand for wireless sensor networks increases the need for new protocols with specific ways of distributing data emerges. Chaos is one of those protocols. Chaos has no native security countermeasures implemented, therefore it is important to test how vulnerable it is against attacks. In this thesis I present four novel attacks to test the robustness of Chaos. Experiments show that a Drizzle-attack was the most effective attack, strategic placement of the nodes was the key to a more efficient attack.
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Van, Heerden Renier Pelser. "A formalised ontology for network attack classification." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1011603.

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One of the most popular attack vectors against computers are their network connections. Attacks on computers through their networks are commonplace and have various levels of complexity. This research formally describes network-based computer attacks in the form of a story, formally and within an ontology. The ontology categorises network attacks where attack scenarios are the focal class. This class consists of: Denial-of- Service, Industrial Espionage, Web Defacement, Unauthorised Data Access, Financial Theft, Industrial Sabotage, Cyber-Warfare, Resource Theft, System Compromise, and Runaway Malware. This ontology was developed by building a taxonomy and a temporal network attack model. Network attack instances (also know as individuals) are classified according to their respective attack scenarios, with the use of an automated reasoner within the ontology. The automated reasoner deductions are verified formally; and via the automated reasoner, a relaxed set of scenarios is determined, which is relevant in a near real-time environment. A prototype system (called Aeneas) was developed to classify network-based attacks. Aeneas integrates the sensors into a detection system that can classify network attacks in a near real-time environment. To verify the ontology and the prototype Aeneas, a virtual test bed was developed in which network-based attacks were generated to verify the detection system. Aeneas was able to detect incoming attacks and classify them according to their scenario. The novel part of this research is the attack scenarios that are described in the form of a story, as well as formally and in an ontology. The ontology is used in a novel way to determine to which class attack instances belong and how the network attack ontology is affected in a near real-time environment.
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Tan, Hailun Computer Science &amp Engineering Faculty of Engineering UNSW. "Secure network programming in wireless sensor networks." Awarded By:University of New South Wales. Computer Science & Engineering, 2010. http://handle.unsw.edu.au/1959.4/44835.

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Network programming is one of the most important applications in Wireless Sensor Networks as It provides an efficient way to update program Images running on sensor nodes without physical access to them. Securing these updates, however, remains a challenging and important issue, given the open deployment environment of sensor nodes. Though several security schemes have been proposed to impose the authenticity and Integrity protection on network programming applications, they are either energy Inefficient as they tend to use digital signature or lacks the data confidentiality. In addition, due to the absence of secure memory management in the current sensor hardware, the attacker could inject malicious code into the program flash by exploiting buffer overflow In the memory despite the secure code dissemination. The contribution of this thesis Is to provide two software-based security protocols and one hardware-based remote attestation protocol for network programming application. Our first protocol deploys multiple one-way key chains for a multi-hop sensor network. The scheme Is shown to be lower In computational, power consumption and communication costs yet still able to secure multi??hop propagation of program images. Our second protocol utilizes an Iterative hash structure to the data packets in network programming application, ensuring the data confidentiality and authenticity. In addition, we Integrated confidentiality and DoS-attack-resistance in a multi??hop code dissemination protocol. Our final solution is a hardware-based remote attestation protocol for verification of running codes on sensor nodes. An additional piece of tamper-proof hardware, Trusted Platform Module (TPM), is imposed into the sensor nodes. It secures the sensitive information (e.g., the session key) from attackers and monitors any platform environment changes with the Internal registers. With these features of TPM, the code Injection attack could be detected and removed when the contaminated nodes are challenged in our remote attestation protocol. We implement the first two software-based protocols with Deluge as the reference network programming protocol in TinyOS, evaluate them with the extensive simulation using TOSSIM and validate the simulation results with experiments using Tmote. We implement the remote attestation protocol on Fleck, a sensor platform developed by CSIRO that Integrates an Atmel TPM chip.
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Hamid, Thaier K. A. "Attack graph approach to dynamic network vulnerability analysis and countermeasures." Thesis, University of Bedfordshire, 2014. http://hdl.handle.net/10547/576432.

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It is widely accepted that modern computer networks (often presented as a heterogeneous collection of functioning organisations, applications, software, and hardware) contain vulnerabilities. This research proposes a new methodology to compute a dynamic severity cost for each state. Here a state refers to the behaviour of a system during an attack; an example of a state is where an attacker could influence the information on an application to alter the credentials. This is performed by utilising a modified variant of the Common Vulnerability Scoring System (CVSS), referred to as a Dynamic Vulnerability Scoring System (DVSS). This calculates scores of intrinsic, time-based, and ecological metrics by combining related sub-scores and modelling the problem’s parameters into a mathematical framework to develop a unique severity cost. The individual static nature of CVSS affects the scoring value, so the author has adapted a novel model to produce a DVSS metric that is more precise and efficient. In this approach, different parameters are used to compute the final scores determined from a number of parameters including network architecture, device setting, and the impact of vulnerability interactions. An attack graph (AG) is a security model representing the chains of vulnerability exploits in a network. A number of researchers have acknowledged the attack graph visual complexity and a lack of in-depth understanding. Current attack graph tools are constrained to only limited attributes or even rely on hand-generated input. The automatic formation of vulnerability information has been troublesome and vulnerability descriptions are frequently created by hand, or based on limited data. The network architectures and configurations along with the interactions between the individual vulnerabilities are considered in the method of computing the Cost using the DVSS and a dynamic cost-centric framework. A new methodology was built up to present an attack graph with a dynamic cost metric based on DVSS and also a novel methodology to estimate and represent the cost-centric approach for each host’ states was followed out. A framework is carried out on a test network, using the Nessus scanner to detect known vulnerabilities, implement these results and to build and represent the dynamic cost centric attack graph using ranking algorithms (in a standardised fashion to Mehta et al. 2006 and Kijsanayothin, 2010). However, instead of using vulnerabilities for each host, a CostRank Markov Model has developed utilising a novel cost-centric approach, thereby reducing the complexity in the attack graph and reducing the problem of visibility. An analogous parallel algorithm is developed to implement CostRank. The reason for developing a parallel CostRank Algorithm is to expedite the states ranking calculations for the increasing number of hosts and/or vulnerabilities. In the same way, the author intends to secure large scale networks that require fast and reliable computing to calculate the ranking of enormous graphs with thousands of vertices (states) and millions of arcs (representing an action to move from one state to another). In this proposed approach, the focus on a parallel CostRank computational architecture to appraise the enhancement in CostRank calculations and scalability of of the algorithm. In particular, a partitioning of input data, graph files and ranking vectors with a load balancing technique can enhance the performance and scalability of CostRank computations in parallel. A practical model of analogous CostRank parallel calculation is undertaken, resulting in a substantial decrease in calculations communication levels and in iteration time. The results are presented in an analytical approach in terms of scalability, efficiency, memory usage, speed up and input/output rates. Finally, a countermeasures model is developed to protect against network attacks by using a Dynamic Countermeasures Attack Tree (DCAT). The following scheme is used to build DCAT tree (i) using scalable parallel CostRank Algorithm to determine the critical asset, that system administrators need to protect; (ii) Track the Nessus scanner to determine the vulnerabilities associated with the asset using the dynamic cost centric framework and DVSS; (iii) Check out all published mitigations for all vulnerabilities. (iv) Assess how well the security solution mitigates those risks; (v) Assess DCAT algorithm in terms of effective security cost, probability and cost/benefit analysis to reduce the total impact of a specific vulnerability.
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Schuhart, Russell G. "Hacking social networks examining the viability of using computer network attack against social networks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FSchuhart.pdf.

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Thesis (M.S. in Information Systems and Operations)--Naval Postgraduate School, March 2007.
Thesis Advisor(s): David Tucker. "March 2007." Includes bibliographical references (p. 55-56). Also available in print.
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Klaus, Christian. "Network design for reliability and resilience to attack." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/41406.

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Approved for public release; distribution is unlimited.
We define and solve two network-design problems. In the first, (1) a defender uses limited resources to select a portfolio of paths or design a sub-network; (2) an attacker then uses limited attack resources to destroy network arcs, and then (3) the defender operates the damaged network optimally by ending a shortest path. The solution identifies a network design that minimizes post-attack path length. We show how the tri-level problem is equivalent to a single-level mixed integer program (MIP) with an exponential number of rows and columns, and solve that MIP using simultaneous row and column generation. Methods extend to network operations denied through general now constructs. The second problem considers a stochastic logistics network where arcs are present randomly and independently. Shipping from a source to a destination may be delayed until a path connecting the two is available. In the presence of storage capacity, cargo can be shipped partway. The problem's solution identifies the storage locations that minimize the cargo's waiting time for shipment. We develop and demonstrate practical methods to solve this #P-complete problem on a model instance derived from a Department of Defense humanitarian shipping network.
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Mohammadnia, Hamzeh. "IoT-NETZ: Spoong Attack Mitigation in IoT Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260250.

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The phenomenal growth of the Internet of Things (IoT) and popularity of the mobile stations have rapidly increased the demand of WLAN network (known as IEEE 802.11 and WiFi). WLAN is a low-cost alternative of the cellular network and being an unlicensed spectrum to build the master plan of embedding the Internet in everything -&-anywhere. At the same time, monitoring the number of IoT and WiFi-enabled devices across residential and enterprises is not trivial. Therefore, future WiFi network architecture requires an agile management paradigm to provide internal support and security for WiFi networks.The operation of IoT and mobile device applications relies on scalability and high-performance computing of clouds. Cloud computing has completely centralized the current data center networking architecture and it provides computation-intensive, high-speed network, and realtime responses to the requests of IoT. The IoT-to-cloud communication is the essence of network security concerns and it is in grievous need of constant security improvement along the inter-networking. Based on the number of researches and analysis on generated traffic by IoT, it has been observed there are the significant number of massive spoofing-oriented attacks targeting cloud services are launched from compromised IoT.On the basis of reviewing prior researches on mostly-conducted network attacks by IoT, there is a challenging and common characteristic which has been frequently utilized in the numerous massive Internet attacks, known as spoofing. This work will survey the existing proposed solutions which have been deployed to protect both traditional and softwarized network paradigms. Then, it proposes the approach of this work that enables IoT-hosting networks protected by employing Software-defined Wireless Networking (SDWN) within the proposed model to mitigate spoofing -oriented network attacks. In addition, the proposed solution provides the environmental sustainability feature by saving power consumption in networking devices during network operation. The practical improvement in the proposed model is measured and evaluated within the emulated environment of Mininet-WiFi.
Den fenomenala tillväxten av IoT och populariteten hos mobilstationerna har snabbt ökat efterfrågan på WLAN-nätverk (känd som IEEE 802.11 och WiFi). WLAN är ett billigt alternativ för mobilnätet och är ett olicensierat spektrum för att bygga huvudplanen för att bädda in Internet i allt-och-var som helst. Samtidigt är det inte trivialt att övervaka antalet IoT och WiFi-aktiverade enheter över bostäder och företag. Därför kräver framtida WiFi nätverksarkitektur ett smidigt hantering paradigm för att tillhandahålla internt stöd och säkerhet för WiFi-nätverk.Användningen av IoT och mobilanvändningsapplikationer är beroende av skalbarhet och högpresterande beräkningar av moln. Cloud computing har helt centraliserat den nuvarande datacenters nätverksarkitektur och det ger beräkningsintensiva, höghastighetsnätverk och realtidssvar påbegäran från IoT. IoT-till-moln kommunikationen är kärnan i nätverkssäkerhetshänsyn och de har ett allvarligt behov av ständig förbättring och säkerhetshärdning inom deras internätverk. Baserat på antalet undersökningar och analyser av genererad trafik av IoT har det observerats. Det finns det betydande antalet massiva spoofing-orienterade attacker som riktar sig mot molntjänster, lanseras från komprometterad IoT.På grundval av att granska tidigare undersökningar om IoTs mest genomförda nätverksattacker finns det en utmanande och gemensam egenskap som ofta utnyttjats i de många massiva internetattackerna. Detta arbete kommer att undersöka de befintliga lösningarna som har implementerats för att skydda både traditionella och mjukvariga nätverksparadigmer. Därefter föreslår det tillvägagångssättet för detta arbete som möjliggör IoT-värdnät skyddade genom att använda SDWN inom den föreslagna modellen för att mildra poofing-orienterade nätverksattacker. Dessutom erbjuder den föreslagna lösningen miljöhållbarhet genom att spara strömförbrukning i nätverksenheter under nätverksdrift. Den praktiska förbättringen av den föreslagna modellen mäts och utvärderas inom den omgivande miljön av Mininet-WiFi.
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Kenar, Serkan. "An Extensible Framework For Automated Network Attack Signature Generation." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611418/index.pdf.

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The effectiveness of misuse-based intrusion detection systems (IDS) are seriously broken, with the advance of threats in terms of speed and scale. Today worms, trojans, viruses and other threats can spread all around the globe in less than thirty minutes. In order to detect these emerging threats, signatures must be generated automatically and distributed to intrusion detection systems rapidly. There are studies on automatically generating signatures for worms and attacks. However, either these systems rely on Honeypots which are supposed to receive only suspicious traffic, or use port-scanning outlier detectors. In this study, an open, extensible system based on an network IDS is proposed to identify suspicious traffic using anomaly detection methods, and to automatically generate signatures of attacks out of this suspicious traffic. The generated signatures are classified and fedback into the IDS either locally or distributed. Design and proof-of-concept implementation are described and developed system is tested on both synthetic and real network data. The system is designed as a framework to test different methods and evaluate the outcomes of varying configurations easily. The test results show that, with a properly defined attack detection algorithm, attack signatures could be generated with high accuracy and efficiency. The resulting system could be used to prevent early damages of fast-spreading worms and other threats.
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Books on the topic "Attack on the network"

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College), Symposium on Computer Network Attack and International Law (1999 Naval War. Computer network attack and international law. Newport, R. I: Naval War College, 2002.

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Harrison, R. F. Neur al networks,heart attack and bayesian decisions: An application oof the Boltzmann perceptron network. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1994.

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Prowell, Stacy J. Seven deadliest network attacks. Burlington, MA: Syngress, 2010.

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Monte, Matthew. Network Attacks & Exploitation. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119183440.

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Rob, Kraus, and Borkin Mike, eds. Seven deadliest network attacks. Amsterdam: Syngress, 2010.

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Network security attacks and countermeasures. Hershey, PA: Information Science Reference, 2015.

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Perez, Richard, 1969 Oct. 17- and Ely Adam, eds. Seven deadliest social network attacks. Amsterdam: Syngress, 2010.

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Karppinen, Kaarina. Security measurement based on attack trees in a mobile ad hoc network environment. [Espoo, Finland]: VTT Technical Research Centre of Finland, 2005.

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Wilson, Clay. Computer attack and cyberterrorism. NY: Nova Science Publishers, 2009.

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Zhou, Qing. Network Robustness under Large-Scale Attacks. New York, NY: Springer New York, 2013.

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Book chapters on the topic "Attack on the network"

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Wang, Lingyu, Massimiliano Albanese, and Sushil Jajodia. "Attack Graph and Network Hardening." In Network Hardening, 15–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04612-9_3.

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Mendel, Florian, Tomislav Nad, and Martin Schläffer. "Collision Attack on Boole." In Applied Cryptography and Network Security, 369–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01957-9_23.

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Yang, Qing, and Lin Huang. "Mobile Network Security." In Inside Radio: An Attack and Defense Guide, 267–342. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8447-8_8.

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Theodoros, Tzouramanis, and Karampelas Loukas. "Online Social Network Phishing Attack." In Encyclopedia of Social Network Analysis and Mining, 1200–1206. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_348.

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Theodoros, Tzouramanis, and Karampelas Loukas. "Online Social Network Phishing Attack." In Encyclopedia of Social Network Analysis and Mining, 1671–77. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_348.

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Theodoros, Tzouramanis, and Karampelas Loukas. "Online Social Network Phishing Attack." In Encyclopedia of Social Network Analysis and Mining, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_348-1.

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Lee, Heejo, and Jong Kim. "Attack Resiliency of Network Topologies." In Parallel and Distributed Computing: Applications and Technologies, 638–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30501-9_123.

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Yu, Hongbo, Gaoli Wang, Guoyan Zhang, and Xiaoyun Wang. "The Second-Preimage Attack on MD4." In Cryptology and Network Security, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11599371_1.

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Todo, Yosuke, and Kazumaro Aoki. "FFT Key Recovery for Integral Attack." In Cryptology and Network Security, 64–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12280-9_5.

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Yang, Lin, Meiqin Wang, and Siyuan Qiao. "Side Channel Cube Attack on PRESENT." In Cryptology and Network Security, 379–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10433-6_25.

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Conference papers on the topic "Attack on the network"

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Alzubi, Saif, Frederic T. Stahl, and Mohamed M. Gaber. "Towards Intrusion Detection Of Previously Unknown Network Attacks." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0035.

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Advances in telecommunication network technologies have led to an ever more interconnected world. Accordingly, the types of threats and attacks to intrude or disable such networks or portions of it are continuing to develop likewise. Thus, there is a need to detect previously unknown attack types. Supervised techniques are not suitable to detect previously not encountered attack types. This paper presents a new ensemble-based Unknown Network Attack Detector (UNAD) system. UNAD proposes a training workflow composed of heterogeneous and unsupervised anomaly detection techniques, trains on attack-free data and can distinguish normal network flow from (previously unknown) attacks. This scenario is more realistic for detecting previously unknown attacks than supervised approaches and is evaluated on telecommunication network data with known ground truth. Empirical results reveal that UNAD can detect attacks on which the workflows have not been trained on with a precision of 75% and a recall of 80%. The benefit of UNAD with existing network attack detectors is, that it can detect completely new attack types that have never been encountered before.
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Chen, Yu, Shahin Jabbari, Michael Kearns, Sanjeev Khanna, and Jamie Morgenstern. "Network Formation under Random Attack and Probabilistic Spread." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/26.

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We study a network formation game where agents receive benefits by forming connections to other agents but also incur both direct and indirect costs from the formed connections. Specifically, once the agents have purchased their connections, an attack starts at a randomly chosen vertex in the network and spreads according to the independent cascade model with a fixed probability, destroying any infected agents. The utility or welfare of an agent in our game is defined to be the expected size of the agent's connected component post-attack minus her expenditure in forming connections. Our goal is to understand the properties of the equilibrium networks formed in this game. Our first result concerns the edge density of equilibrium networks. A network connection increases both the likelihood of remaining connected to other agents after an attack as well the likelihood of getting infected by a cascading spread of infection. We show that the latter concern primarily prevails and any equilibrium network in our game contains only $O(n\log n)$ edges where $n$ denotes the number of agents. On the other hand, there are equilibrium networks that contain $\Omega(n)$ edges showing that our edge density bound is tight up to a logarithmic factor. Our second result shows that the presence of attack and its spread through a cascade does not significantly lower social welfare as long as the network is not too dense. We show that any non-trivial equilibrium network with $O(n)$ edges has $\Theta(n^2)$ social welfare, asymptotically similar to the social welfare guarantee in the game without any attacks.
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Isaac Gabriel, Odun-Ayo. "A Review of Common Tools and Techniques for Reconnaissance Attacks.. Proceedings of the." In 28th iSTEAMS Multidisciplinary Research Conference AIUWA The Gambia. Society for Multidisciplinary and Advanced Research Techniques - Creative Research Publishers, 2021. http://dx.doi.org/10.22624/aims/isteams-2021/v28p11.

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Background: A reconnaissance helps the attacker gain valuable information on the target to help the attacker select the best tools that would make the attack successful. Aim: This study aims to review tools for reconnaissance attacks which will be beneficial to professional ethical hackers and also enlighten organizations and the general public of the potential harm of successful reconnaissance attacks. Methodology: The databases – Springer, Elsevier, Wiley, IEEE, ACM, ArXiv, and Google Scholar were explored. A quantitative evaluation was conducted on 19 selected articles. Result: 95.2% of the reconnaissance tools allowed experts to gather information by use of the command line. While 4.8% of the tools do not provide a commandline interface. 61.9% of the tools are network-based – can be used to gather data about the target’s network infrastructure. Conclusion: The best-fit tool is massively dependent on the attacker or penetration tester. Therefore, a tool should be selected based on the user’s preference and the attack style. Keywords: Reconnaissance, cyber-attack, cybersecurity,social engineering, techniques.
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Ingols, Kyle, Matthew Chu, Richard Lippmann, Seth Webster, and Stephen Boyer. "Modeling Modern Network Attacks and Countermeasures Using Attack Graphs." In 2009 Annual Computer Security Applications Conference (ACSAC). IEEE, 2009. http://dx.doi.org/10.1109/acsac.2009.21.

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Lei, Jie, and Zhi-tang Li. "Using Network Attack Graph to Predict the Future Attacks." In 2007 Second International Conference on Communications and Networking in China. IEEE, 2007. http://dx.doi.org/10.1109/chinacom.2007.4469413.

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Li, Man, and Jinjing Cao. "Network Attack Defense Awareness." In Next Generation Computer and Information Technology 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.111.34.

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Fu, Yangyang, Zheng O’Neill, Jin Wen, and Veronica Adetola. "Evaluating the Impact of Cyber-Attacks on Grid-Interactive Efficient Buildings." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-73694.

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Abstract Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.
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Wang, Weifeng, Xinyu Zhang, Likai Dong, Yuling Fan, Xinyi Diao, and Tao Xu. "Network Attack Detection based on Domain Attack Behavior Analysis." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263663.

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Kasthurirathna, Dharshana, Andy Dong, Mahendrarajah Piraveenan, and Irem Y. Tumer. "The Failure Tolerance of Mechatronic Software Systems to Random and Targeted Attacks." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12188.

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This paper describes a complex networks approach to study the failure tolerance of mechatronic software systems under various types of hardware and/or software failures. We produce synthetic system architectures based on evidence of modular and hierarchical modular product architectures and known motifs for the interconnection of physical components to software. The system architectures are then subject to various forms of attack. The attacks simulate failure of critical hardware or software. Four types of attack are investigated: degree centrality, betweenness centrality, closeness centrality and random attack. Failure tolerance of the system is measured by a ‘robustness coefficient’, a topological ‘size’ metric of the connectedness of the attacked network. We find that the betweenness centrality attack results in the most significant reduction in the robustness coefficient, confirming betweenness centrality, rather than the number of connections (i.e. degree), as the most conservative metric of component importance. A counter-intuitive finding is that “designed” system architectures, including a bus, ring, and star architecture, are not significantly more failure-tolerant than interconnections with no prescribed architecture, that is, a random architecture. Our research provides a data-driven approach to engineer the architecture of mechatronic software systems for failure tolerance.
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Trifonov, R., D. Gotseva, and P. Stoynov. "Brute Force Network Attack Detection through Neural Networks." In 2021 XXX International Scientific Conference Electronics (ET). IEEE, 2021. http://dx.doi.org/10.1109/et52713.2021.9579905.

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Reports on the topic "Attack on the network"

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May, Jack, and James Petersen. Network Attack Program. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada387940.

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Lenderman, Curtis C. Computer Network Attack: An Operational Tool? Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada415427.

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Busby, Daniel J. Peacetime Use of Computer Network Attack. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada377624.

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Tayeb, Shahab. Protecting Our Community from the Hidden Vulnerabilities of Today’s Intelligent Transportation Systems. Mineta Transportation Institute, May 2022. http://dx.doi.org/10.31979/mti.2022.2132.

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The ever-evolving technology interwoven into the transportation industry leaves it frequently at risk for cyber-attacks. This study analyzes the security of a common in-vehicle network, the Controller Area Network (CAN), standard in most vehicles being manufactured today. Like many other networks, CAN comes with inherent vulnerabilities that leave CAN implementations at risk of being targeted by cybercriminals. Such vulnerabilities range from eavesdropping, where the attacker can read the raw data traversing the vehicle, to spoofing, where the attacker can place fabricated traffic on the network. The research team initially performed a simulation of CAN traffic generation followed by hardware implementation of the same on a test vehicle. Due to the concealed and untransparent nature of CAN, the team reverse-engineered the missing parameters through a series of passive "sniffing attacks" (attacks using data reading utilities called packet sniffers) on the network and then demonstrated the feasibility of the attack by placing fabricated frames on the CAN.
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Salowey, J., and S. Hanna. The Network Endpoint Assessment (NEA) Asokan Attack Analysis. RFC Editor, December 2012. http://dx.doi.org/10.17487/rfc6813.

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Kahn, Russell L. Attack Analyzer: A Network Analysis and Visualization Tool. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada464353.

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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a sufficient number of solutions and algorithms for training, designing and testing various types of neural networks. Software architecture is presented using a procedural-object approach. Because there is no need to save intermediate result of the program (after learning entire perceptron is stored in the file), all the progress of learning is stored in the normal files on hard disk.
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FU, Fabian. End-to-End and Network-wide Attack Defense Solution -Overhaul Carrier Network Security. Denmark: River Publishers, July 2016. http://dx.doi.org/10.13052/popcas006.

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Williamson, Jennie M. Information Operations: Computer Network Attack in the 21st Century. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada402018.

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Hanson, Kraig. Organization of DoD Computer Network Defense, Exploitation, and Attack Forces. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada500822.

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