Relevant bibliographies by topics / NETWORK INTRUSION RESPONSE

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'NETWORK INTRUSION RESPONSE'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "NETWORK INTRUSION RESPONSE"

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Reshmi, B. M., S. S. Manvi, and Bhagyavati. "An Agent Based Intrusion Detection Model for Mobile Ad Hoc Networks." Mobile Information Systems 2, no. 4 (2006): 169–91. http://dx.doi.org/10.1155/2006/921047.

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Intrusion detection has over the last few years, assumed paramount importance within the broad realm of network security, more so in case of wireless mobile ad hoc networks. The inherently vulnerable characteristics of wireless mobile ad hoc networks make them susceptible to attacks in-spite of some security measures, and it may be too late before any counter action can take effect. As such, there is a need to complement traditional security mechanisms with efficient intrusion detection and response systems. This paper proposes an agent-based model to address the aspect of intrusion detection in cluster based mobile wireless ad hoc network environment. The model comprises of a set of static and mobile agents, which are used to detect intrusions, respond to intrusions, and distribute selected and aggregated intrusion information to all other nodes in the network in an intelligent manner. The model is simulated to test its operation effectiveness by considering the performance parameters such as, detection rate, false positives, agent overheads, and intrusion information distribution time. Agent based approach facilitates flexible and adaptable security services. Also, it supports component based software engineering components such as maintainability, reachability, reusability, adaptability, flexibility, and customization.
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Aranganathan, A., and C. D. Suriyakala. "Agent based secure intrusion detection and prevention for rushing attacks in clustering MANETs." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 22. http://dx.doi.org/10.14419/ijet.v7i2.20.11736.

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Intrusion detection is one of challenging issues in wireless networks. The inherently vulnerable characteristics of wireless mobile ad hoc networks make them susceptible to attacks in-spite of some security measures, and it may be too late before any counter action can take effect. As such, there is a need to complement security mechanisms with efficient intrusion detection and response systems. This paper proposes an agent-based model to address the aspect of intrusion detection in cluster based Mobile ad hoc network environment. The model comprises of mobile agents, which are used to detect intrusions, respond to intrusions, mainly preventing the routing attacks while securing them and distributing selected and aggregated intrusion information to all other nodes in the network in an intelligent manner to compensate the attack. The model is simulated to test its operation effectiveness by considering various performance parameters such as, packet delivery ratio, communication overhead, throughput. It implements a secure detection and prevention technique that contains the Blowfish algorithm which is a symmetric encryption and decryption algorithm having a secure standard till date against attacks to make the network transmission secure while monitoring malicious nodes and preventing them from compromising the integrity of the network. Agent based approach facilitates flexible and adaptable security services. Also, it supports component based software engineering components such as maintainability, reachability, reusability, adaptability, and flexibility.
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Cai, Yu. "Mobile Agent Based Network Defense System in Enterprise Network." International Journal of Handheld Computing Research 2, no. 1 (January 2011): 41–54. http://dx.doi.org/10.4018/jhcr.2011010103.

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Security has become the Achilles’ heel of many organizations in today’s computer-dominated society. In this paper, a configurable intrusion detection and response framework named Mobile Agents based Distributed (MAD) security system was proposed for enterprise network consisting of a large number of mobile and handheld devices. The key idea of MAD is to use autonomous mobile agents as lightweight entities to provide unified interfaces for intrusion detection, intrusion response, information fusion, and dynamic reconfiguration. These lightweight agents can be easily installed and managed on mobile and handheld devices. The MAD framework includes a family of autonomous agents, servers and software modules. An Object-based intrusion modeling language (mLanguage) is proposed to allow easy data sharing and system control. A data fusion engine (mEngine) is used to provide fused results for traffic classification and intrusion identification. To ensure Quality-of-Service (QoS) requirements for end users, adaptive resource allocation scheme is also presented. It is hoped that this project will advance the understanding of complex, interactive, and collaborative distributed systems.
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Richard Maina Rimiru, Guanzheng Tan, and Cheruiyot Wilson. "Innate-Inspired Automated Intrusion Response Mechanism for a Network Intrusion Detection System." Journal of Convergence Information Technology 7, no. 9 (May 31, 2012): 194–201. http://dx.doi.org/10.4156/jcit.vol7.issue9.24.

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Braithwaite, Imothy. "Understanding Network Security Monitoring and Intrusion Response (NSMIR)." EDPACS 28, no. 8 (February 2001): 1–12. http://dx.doi.org/10.1201/1079/43265.28.8.20010201/30381.1.

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Shameli-Sendi, Alireza, Julien Desfossez, Michel Dagenais, and Masoume Jabbarifar. "A Retroactive-Burst Framework for Automated Intrusion Response System." Journal of Computer Networks and Communications 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/134760.

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The aim of this paper is to present an adaptive and cost-sensitive model to prevent security intrusions. In most automated intrusion response systems, response selection is performed locally based on current threat without using the knowledge of attacks history. Another challenge is that a group of responses are applied without any feedback mechanism to measure the response effect. We address these problems through retroactive-burst execution of responses and a Response Coordinator (RC) mechanism, the main contributions of this work. The retroactive-burst execution consists of several burst executions of responses with, at the end of each burst, a mechanism for measuring the effectiveness of the applied responses by the risk assessment component. The appropriate combination of responses must be considered for each burst execution to mitigate the progress of the attack without necessarily running the next round of responses, because of the impact on legitimate users. In the proposed model, there is a multilevel response mechanism. To indicate which level is appropriate to apply based on the retroactive-burst execution, we get help from a Response Coordinator mechanism. The applied responses can improve the health of Applications, Kernel, Local Services, Network Services, and Physical Status. Based on these indexes, the RC gives a general overview of an attacker’s goal in a distributed environment.
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Malialis, Kleanthis, Sam Devlin, and Daniel Kudenko. "Distributed reinforcement learning for adaptive and robust network intrusion response." Connection Science 27, no. 3 (April 15, 2015): 234–52. http://dx.doi.org/10.1080/09540091.2015.1031082.

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Jiang, Xue Song, Xiu Mei Wei, and Yu Shui Geng. "The Research of Intrusion Detection System Based on ANN on Cloud Platform." Applied Mechanics and Materials 263-266 (December 2012): 2962–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2962.

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Intrusion detection system (IDS) can find the intrusion information before the computer be attacked, and can hold up and response the intrusion in real time. Artificial neural network algorithms play a key role in IDS. The intrusion detection system (ANN) algorithms can analyze the captured data and judge whether the data is intrusion. In this paper we used Back Propagation (BP) network and Radical Basis Function (RBF) network to the IDS. The result of the experiment improve that The RBF neural network is better than BP neural network in the ability of approximation, classification and learning speed. During the procedure there is a large amount of computes. On cloud platform the calculation speed has been greatly increased. So that we can find the invasion more quickly and do the processing works accordingly.
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G. Murugan, Dr. "Improve secure based multi-path routing to mitigate the intrusion endurance in heterogeneous wireless sensor networks." International Journal of Engineering & Technology 7, no. 4 (September 26, 2018): 2746. http://dx.doi.org/10.14419/ijet.v7i4.17957.

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Wireless Sensor Networks (WSNs) have many potential applications. Multi-path routing is widely used in WSN to achieve reliability and perform Fault Tolerance. Multi-path routing determines and assigns multiple routes from a given sensor node to the sink. The transmission of data among the multi-path brings path redundancy, which increases the reliability and reduces the network congestion. In this research work, a dynamic redundancy management algorithm is proposed. To exploit multi-path routing in order to process the user request with existence of defective and malicious nodes. The objective of this work is to analyze the trade-off between energy consumption and Quality of Service (QoS) gain in security and reliability in order to increase the lifetime. The optimized redundancy level of multipath routing is determined dynamically which is used to improve the query response while extending the network lifetime and also for detecting intrusions and send alert to the system through Intrusion Detection System (IDS). Then, a voting-based distributed Intrusion Detection (ID) algorithm is proposed to detect and remove malicious nodes in a sensor network. The malicious node has been determined through number of voters using voting-based distributed ID algorithm. The efficient redundancy management of a clustered Heterogeneous Wireless Sensor Network (HWSN) is to increase the network lifetime in the presence of unreliable and malicious nodes. Therefore, the reliability improved dramatically.
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An, Xingshuo, Fuhong Lin, Shenggang Xu, Li Miao, and Chao Gong. "A Novel Differential Game Model-Based Intrusion Response Strategy in Fog Computing." Security and Communication Networks 2018 (August 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/1821804.

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Fog computing is an emerging network paradigm. Due to its characteristics (e.g., geo-location and constrained resource), fog computing is subject to a broad range of security threats. Intrusion detection system (IDS) is an essential security technology to deal with the security threats in fog computing. We have introduced a fog computing IDS (FC-IDS) framework in our previous work. In this paper, we study the optimal intrusion response strategy in fog computing based on the FC-IDS scheme proposed in our previous work. We postulate the intrusion process in fog computing and describe it with a mathematical model based on differential game theory. According to this model, the optimal response strategy is obtained corresponding to the optimal intrusion strategy. Theoretical analysis and simulation results demonstrate that our security model can effectively stabilize the intrusion frequency of the invaders in fog computing.
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Dissertations / Theses on the topic "NETWORK INTRUSION RESPONSE"

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Malialis, Kleanthis. "Distributed reinforcement learning for network intrusion response." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/8109/.

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The increasing adoption of technologies and the exponential growth of networks has made the area of information technology an integral part of our lives, where network security plays a vital role. One of the most serious threats in the current Internet is posed by distributed denial of service (DDoS) attacks, which target the availability of the victim system. Such an attack is designed to exhaust a server's resources or congest a network's infrastructure, and therefore renders the victim incapable of providing services to its legitimate users or customers. To tackle the distributed nature of these attacks, a distributed and coordinated defence mechanism is necessary, where many defensive nodes, across different locations cooperate in order to stop or reduce the flood. This thesis investigates the applicability of distributed reinforcement learning to intrusion response, specifically, DDoS response. We propose a novel approach to respond to DDoS attacks called Multiagent Router Throttling. Multiagent Router Throttling provides an agent-based distributed response to the DDoS problem, where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. One of the novel characteristics of the proposed approach is that it has a decentralised architecture and provides a decentralised coordinated response to the DDoS problem, thus being resilient to the attacks themselves. Scalability constitutes a critical aspect of a defence system since a non-scalable mechanism will never be considered, let alone adopted, for wide deployment by a company or organisation. We propose Coordinated Team Learning (CTL) which is a novel design to the original Multiagent Router Throttling approach based on the divide-and-conquer paradigm, that uses task decomposition and coordinated team rewards. To better scale-up CTL is combined with a form of reward shaping. The scalability of the proposed system is successfully demonstrated in experiments involving up to 1000 reinforcement learning agents. The significant improvements on scalability and learning speed lay the foundations for a potential real-world deployment.
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Karunanidhi, Karthikeyan. "ARROS; distributed adaptive real-time network intrusion response." Ohio : Ohio University, 2006. http://www.ohiolink.edu/etd/view.cgi?ohiou1141074467.

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Stanley, Fred Philip. "Intrusion detection and response for system and network attacks." [Ames, Iowa : Iowa State University], 2009.

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

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A mobile ad hoc network (MANET) consists of a group of autonomous mobile nodes with no infrastructure support. In this research, we develop a distributed intrusion detection and response system for MANET, and we believe it presents a second line of defense that cannot be replaced by prevention schemes. We based our detection framework on the study of attack taxonomy. We then propose a set of detection methods suitable of detecting different attack categories. Our approaches are based on protocol specification analysis with categorical and statistical measures. Node-based approaches may be too restrictive in scenarios where attack patterns cannot be observed by any isolated node. Therefore, we have developed cooperative detection approaches for a more effective detection model. One approach is to form IDS clusters by grouping nearby nodes, and information can be exchanged within clusters. The cluster-based scheme is more efficient in terms of power consumption and resource utilization, it is also proved resilient against common security compromises without changing the decentralized assumption. We further address two response techniques, traceback and filtering. Existing traceback systems are not suitable for MANET because they rely on incompatible assumptions such as trustworthy routers and static route topology. Our solution, instead, adapts to dynamic topology with no infrastructure requirement. Our solution is also resilient in the face of arbitrary number of collaborative adversaries. We also develop smart filtering schemes to maximize the dropping rate of attack packets while minimizing the dropping rate of normal packets with real-time guarantee. To validate our research, we present case study using both ns-2 simulation and MobiEmu emulation platform with three ad hoc routing protocols: AODV, DSR and OLSR. We implemented various representative attacks based on the attack taxonomy. Our experiments show very promising results using node-based and cluster-based approaches.
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Cannady, James D. Jr. "An Adaptive Neural Network Approach to Intrusion Detection and Response." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/443.

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Computer network attacks seek to achieve one or more objectives against the targeted system. The attack may be designed to gain access to sensitive data, modify records, or conduct activities designed to deny authorized users access to system resources. An effective defense against these incidents requires both the timely and accurate detection of the events and a response to the incident that mitigates the damage caused by the attack. While there is an increasing need for a system capable of accurately identifying network attacks there are very few effective methods capable of detecting these incidents. The constantly changing nature of network attacks requires a flexible defensive system that is capable of analyzing the enormous amount of network traffic and identifying attacks from the available data. The ability to effectively respond to an attack after it has been detected is also very limited. As a result, a rapid and well-organized attack can result in substantial damage to a targeted system before defensive measures can be activated. The goal of this research was the design of an innovative approach to the protection of computer networks that used adaptive neural network techniques to identify and respond to attempts to deny authorized users access to system resources. Since it is impossible to represent all of the possible system states and types of attacks that could occur the ability of the neural network-based system to adapt to changes in the network environment depended upon an incremental learning capability that was developed as part of this research. The adaptive neural network system incorporated a modified reinforcement learning approach to enhance the identification of new network attacks. This capability allowed the intrusion detection system to autonomously improve its analytical ability in response to changes in the threats against the protected network and then take an action that minimized the damage to the protected system. A prototype adaptive neural network architecture was implemented and evaluated in a simulated computer network environment.
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SANTOS, Glenda de Lourdes Ferreira dos. "RESPOSTAS AUTOMÁTICAS PARA MELHORIA DA SEGURANÇA EM SISTEMAS DE DETECÇÃO DE INTRUSOS." Universidade Federal do Maranhão, 2003. http://tedebc.ufma.br:8080/jspui/handle/tede/366.

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Made available in DSpace on 2016-08-17T14:52:54Z (GMT). No. of bitstreams: 1 Glenda de Lourdes Ferreira dos Santos.pdf: 972743 bytes, checksum: 111a2522d029325d266db2465a430638 (MD5) Previous issue date: 2003-11-21
The development of approaches for proving fast reactions against intruders and attackers have been one of the most important requirements in the critical defense of computer networks, since the intrusion occur quickly, demanding reactions without human intervention. These approaches should be able to, autonomously, respond to attacks and deal with several important aspects of the computer security problem in order to reduce the system administrator s workload Such approaches can offer larger reliability and effectiveness in the detection and response processes, a higher rate of security to private networks, better defense possibilities and, in addition, minimize the intruder's change of success. This research work deals with the specification of a society of intelligent agents for assessment and enhancement of intrusion response systems in computer networks. The proposal of the model of intrusion response system (IRS) be based on in several available architectures, in order to look for better solutions for the problems faced in the modelling of a system of that level. With that, was modeled a system to approach the main desirable functionalities for a system of active answers. The system, as part of the NIDIA (Network Intrusion Detection System based on Intelligent Agents) (Lima, 2001), is formed by a society of agents that are responsible for the functions of identification of the characteristic of the attack, choice of the best reaction strategy and for the execution of the response.The society is composed by agents able to determine and apply automatically corrective actions against attacks classified according to a given severity taxonomic model. In the proposed model was looked for to define response to intrusions for abuse and for anomaly to guarantee a lower robustness to the system.
O desenvolvimento de mecanismos para reações rápidas contra intrusos tem sido um dos mais importantes requisitos na defesa crítica de redes de computador, visto que estes agem rapidamente exigindo reações sem intervenção humana. Tais mecanismos devem estar habitas a, automaticamente, responder um ataque e lidar com o vários aspectos do problema de seguança de computadores, e com isso reduzir a carga de trabalho do administrador do sistema. Semelhantes características podem oferecer confiança e efetividade no processo de detecção e resposta, alta taxa de segunça a redes privadas, melhores possibilidades de defesa e, ainda, minimizar as chances do intruso. Essa dissertação trata da especificação de uma sociedade de agentes para a avaliação e aprimoramento de sistema de resposta de intrusão em redes de computadores. A proposta de um modelo de sistema de resposta de intrusao(IRS) é baseada em várias arquiteturas disponíveis na procura da melhor solução para os problemas encontrados na modelagem de um sistema deste nível. Com isso, foi modelado um sistema que contenha as principais funcionalidades desejáveis para um de respostas ativas. O sistema, que faz parte do NIDIA(Network Intrusion Detection System based on Intelligent Agents) (Lima, 2001), é formado por uma sociedade de agentes que são responsáveis pelas funções de identificação das características do ataque, escolha da melhor estratégia de reação a pela execução resposta.A sociedade é composta por agentes artificiais aptos em determinar e aplicar automaticamente ações, corretivas e preventivas, contra ataques classificados de acordo com um modelo taxonômico de severidade. No modelo proposto procurou-se definir respostas de intrusoes por abuso e por anomalia para garantir maior robustes ao sistema.
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Chang, Chia-Ming, and 張家銘. "An Active Network-Based Intrusion Detection and Response System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/97794748462972803444.

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碩士
國立臺灣大學
機械工程學研究所
91
The network security is getting more important because there are increasing worms and network attacks in recent years. More and more security mechanisms are introduced to protect from attack, such as firewalls and intrusion detection systems (IDS). Intrusion detection is defined as the process of monitoring and analyzing events occurs in a computer or network. Intrusion detection system monitors the computer and network traffic for intrusive and suspicious activities. The past works of IDS are mostly focused on passive models, which are aimed at detections and alerts. The passive model is not enough for current network threats. Therefore, this thesis proposes an active network programming model. Comparing to a traditional network, active network gives the nodes programmable ability. It is convenience and flexibility for service provider to develop network services. A prototype of an active network-based intrusion detection and response system (IDRS) is proposed. It adopts the active network technology. The response, service deployment and service update schemes rely on this technology. The proposed IDRS can stop attacks at the first line and respond as fast as possible to reduce the damage caused by intruders. It provides the abilities of detection, report and response. It is also flexible and scalable. The proposed prototype system adopts the novel data mining technology-support vector machine to enhance the detection function. The implementation and experiences of IDRS shows satisfactory results.
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Lee, Yu-Shyang, and 李裕祥. "Response strategy for Adaptive Network Intrusion Response Framework Based on Environment Dependent Risk Analysis." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/28437941034195722026.

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Zhang, Li-zhang, and 張立長. "Risk Model for Adaptive Network Intrusion Response Framework Based on Environment Dependent Risk Analysis." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/29223814078204030884.

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碩士
中原大學
資訊工程研究所
97
Abstract In efforts to defend against attacks in a networked computer environment, more attention is spent on the threat caused by attacks coming from the outside network than on threat caused by attacks originated within the inside network because, in most of the cause, the main sources of the attack are from the outside network. By detecting outside attacks and fail them, most of the security threats to a computer environment are eliminated. However, an outside attack may escape the detection of the defense mechanism and successfully compromise an inside host, launch inside attacks, and cause tremendous damages. Therefore, we need a scheme to measure the risk of inside hosts and, after defense action applied, to determine effectiveness of such defense action. Based on the fact that attacks are usually completed in a sequence or a set of steps, we proposed a risk computation model. The information considered in the model includes vulnerability of hosts, attack type and vulnerability exploited by the attacks, the amount of attack, the configuration of the network environment, and possible progression of attacks. We quantify the risk of a host according to its own characteristics as well as the properties of security alerts. The resulted risk index can be utilized to evaluate the impacts of attacks experienced, predict the future progression of attacks and provide a way to validate the effectiveness of an applied defense strategy.
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Alampalayam, Sathish Kumar. "Intrusion detection and response model for mobile ad hoc networks." 2007. http://etd.louisville.edu/data/UofL0288t2007.pdf.

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Thesis (M.Eng.)--University of Louisville, 2007.
Title and description from thesis home page (viewed December 14, 2007). Department of Computer Engineering and Computer Science. Vita. "May 2007." Includes bibliographical references (p. 163-170).
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Books on the topic "NETWORK INTRUSION RESPONSE"

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Privacy, intrusion detection, and response: Technologies for protecting networks. Hershey, PA: Information Science Reference, 2012.

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Intrusion detection: An introduction to Internet surveillance, correlation, traps, trace back, and response. Sparta, N.J: Intrusion.Net Books, 1998.

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Intrusion Prevention and Active Response: Deploying Network and Host IPS. Syngress, 2005.

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Buchanan, Ben. The Defender’s View. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190665012.003.0004.

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This chapter examines defensive cyber operations in a fashion similar to kill chain analysis. It presents an outline of how baseline network defense is done, and what technologies and techniques contribute to that mission. This includes memory forensics, penetration testing, and incident response. It shows as well how those efforts are likely to be insufficient, and how advanced states have an incentive to go further and intrude into other states’ networks for defensive reasons—operations that are sometimes called counter-computer network exploitation. It is these intrusions, which are genuinely defensive, that can be misperceived and interpreted as offensive intrusions—leading to a cycle of escalation.
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Wayne, Jansen, and National Institute of Standards and Technology (U.S.), eds. Applying mobile agents to intrusion detection and response. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Wayne, Jansen, and National Institute of Standards and Technology (U.S.), eds. Applying mobile agents to intrusion detection and response. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Amoroso, Edward G. Intrusion Detection: An Introduction to Internet Surveillance, Correlation, Trace Back, Traps, and Response. Intrusion.Net Books, 1999.

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Book chapters on the topic "NETWORK INTRUSION RESPONSE"

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Kim, Jinoh, Kiyoung Kim, and Jongsoo Jang. "Policy-Based Intrusion Detection and Automated Response Mechanism." In Information Networking: Wireless Communications Technologies and Network Applications, 399–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45801-8_39.

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Assawakomenkool, Nont, Yash Patel, and Jonathan Voris. "Network Aware Defenses for Intrusion Recognition and Response (NADIR)." In Proceedings of the Future Technologies Conference (FTC) 2018, 226–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02683-7_17.

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Papadaki, Maria, Steven Furnell, Benn Lines, and Paul Reynolds. "Operational Characteristics of an Automated Intrusion Response System." In Communications and Multimedia Security. Advanced Techniques for Network and Data Protection, 65–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45184-6_6.

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Wang, Xinyuan, Douglas S. Reeves, S. Felix Wu, and Jim Yuill. "Sleepy Watermark Tracing: An Active Network-Based Intrusion Response Framework." In IFIP Advances in Information and Communication Technology, 369–84. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/0-306-46998-7_26.

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Kaur, Manpreet, Dale Lindskog, and Pavol Zavarsky. "Integrating Intrusion Response Functionality into the MANET Specific Dynamic Intrusion Detection Hierarchy Architecture." In Ad Hoc Networks, 69–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74439-1_7.

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Dasgupta, Dipankar, and Fabio A. Gonzalez. "An Intelligent Decision Support System for Intrusion Detection and Response." In Information Assurance in Computer Networks, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45116-1_1.

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Yi, Ping, Yiping Zhong, and Shiyong Zhang. "Applying Mobile Agent to Intrusion Response for Ad Hoc Networks." In Lecture Notes in Computer Science, 593–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428848_77.

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Ramachandra, L. Sri, and K. Hareesh. "A Novel Design for Real-Time Intrusion Response in Latest Software-Defined Networks by Graphical Security Models." In Sustainable Communication Networks and Application, 557–68. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8677-4_45.

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Fichera, Joe, and Steven Bolt. "Incident Response." In Network Intrusion Analysis, 33–70. Elsevier, 2013. http://dx.doi.org/10.1016/b978-1-59-749962-0.00003-1.

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"Network inline data modification." In Intrusion Prevention and Active Response, 133–91. Elsevier, 2005. http://dx.doi.org/10.1016/b978-193226647-4/50009-7.

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Conference papers on the topic "NETWORK INTRUSION RESPONSE"

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Klein, Gabriel, Henning Rogge, Felix Schneider, Jens Toelle, Marko Jahnke, and Stefan Karsch. "Response Initiation in Distributed Intrusion Response Systems for Tactical MANETs." In 2010 European Conference on Computer Network Defense (EC2ND). IEEE, 2010. http://dx.doi.org/10.1109/ec2nd.2010.11.

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Samarabandu, Jagath K. "Keynote address: Dynamic network security- intrusion detection and response." In 2016 Moratuwa Engineering Research Conference (MERCon). IEEE, 2016. http://dx.doi.org/10.1109/mercon.2016.7480104.

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Vasilomanolakis, Emmanouil, Michael Stahn, Carlos Garcia Cordero, and Max Muhlhauser. "On probe-response attacks in Collaborative Intrusion Detection Systems." In 2016 IEEE Conference on Communications and Network Security (CNS). IEEE, 2016. http://dx.doi.org/10.1109/cns.2016.7860495.

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Huang, Yih, and Anup K. Ghosh. "Automating Intrusion Response via Virtualization for Realizing Uninterruptible Web Services." In 2009 Eighth IEEE International Symposium on Network Computing and Applications (NCA). IEEE, 2009. http://dx.doi.org/10.1109/nca.2009.37.

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Vasilomanolakis, Emmanouil, Michael Stahn, Carlos Garcia Cordero, and Max Muhlhauser. "Probe-response attacks on collaborative intrusion detection systems: Effectiveness and countermeasures." In 2015 IEEE Conference on Communications and Network Security (CNS). IEEE, 2015. http://dx.doi.org/10.1109/cns.2015.7346892.

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Hidalgo-Espinoza, Sergio, Kevin Chamorro-Cupuerán, and Oscar Chang-Tortolero. "Intrusion Detection in Computer Systems by using Artificial Neural Networks with Deep Learning Approaches." In 10th International Conference on Advances in Computing and Information Technology (ACITY 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101501.

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Abstract:
Intrusion detection into computer networks has become one of the most important issues in cybersecurity. Attackers keep on researching and coding to discover new vulnerabilities to penetrate information security system. In consequence computer systems must be daily upgraded using up-to-date techniques to keep hackers at bay. This paper focuses on the design and implementation of an intrusion detection system based on Deep Learning architectures. As a first step, a shallow network is trained with labelled log-in [into a computer network] data taken from the Dataset CICIDS2017. The internal behaviour of this network is carefully tracked and tuned by using plotting and exploring codes until it reaches a functional peak in intrusion prediction accuracy. As a second step, an autoencoder, trained with big unlabelled data, is used as a middle processor which feeds compressed information and abstract representation to the original shallow network. It is proven that the resultant deep architecture has a better performance than any version of the shallow network alone. The resultant functional code scripts, written in MATLAB, represent a re-trainable system which has been proved using real data, producing good precision and fast response.
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Hooper, Emmanuel. "An Intelligent Intrusion Detection and Response System Using Network Quarantine Channels: Firewalls and Packet Filters." In 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07). IEEE, 2007. http://dx.doi.org/10.1109/mue.2007.81.

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Hooper, Emmanuel. "An Intelligent Intrusion Detection and Response System Using Network Quarantine Channels: Adaptive Policies and Alert Filters." In 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops. IEEE, 2006. http://dx.doi.org/10.1109/wi-iatw.2006.41.

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Sainani, Varsha, and Mei-Ling Shyu. "A Hybrid Layered Multiagent Architecture with Low Cost and Low Response Time Communication Protocol for Network Intrusion Detection Systems." In 2009 International Conference on Advanced Information Networking and Applications. IEEE, 2009. http://dx.doi.org/10.1109/aina.2009.102.

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Jones, C. Birk, Cedric Carter, and Zachary Thomas. "Intrusion Detection & Response using an Unsupervised Artificial Neural Network on a Single Board Computer for Building Control Resilience." In 2018 Resilience Week (RWS). IEEE, 2018. http://dx.doi.org/10.1109/rweek.2018.8473533.

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You might also be interested in the extended bibliographies on the topic 'NETWORK INTRUSION RESPONSE' for particular source types:
Journal articles
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