Abstract
This chapter provides a study regarding the important topics of cyber threat intrusion detection and prevention with regard to identifying malicious incidents, logging information about them, attempting to stop them and reporting the identified malicious attack to the incident response teams. This requires well-selected investigations of cyber threat attacks because cyber-attackers are seeking out and exploiting computer systems and/or networks, application vulnerabilities, and others to attack, causing serious problems for cyber threat attacked public and private organizations’ computer systems or networks. Thus, intrusion detection and prevention strategies are becoming an important knowledge to decide about the right approach to secure critical and crucial infrastructure in public and private organizations against malicious cyber threat attacks.
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References
R. Heady, G. Luger, A.B. Maccabe, M. Servilla, The Architecture of a Network Level Intrusion Detection System, Technical Report 390-20, Department of Computer Science, University of New Mexico, 1990
J. Anderson, An Introduction to Neural Networks (MIT Press, Cambridge, MA, 1995)
K. Kim, M.E. Aminanto, H.C. Tanuwidjaja, Network Intrusion Detection using Deep Learning—A Feature Learning Approach (Springer Nature, Singapore, 2018)
R. Tiwari, R. Kumar, A. Bharti, J. Kishan, Intrusion detection system. Int. J. Techn. Res. Appl. 5, 38–44 (2017)
B. Cappers, Interactive Visualization of Event Log for Cybersecurity, PhD Thesis, TU Eindhoven, 2018
S. Kumar, E.H. Stafford, A pattern matching model for misuse intrusion detection, in Proceedings of the 17th National Computer Security Conference, 1994, pp. 11–21
D.P.F. Möller, R.E. Haas, Guide to Automotive Connectivity and Cybersecurity—Trends, Technologies, Innovations, and Applications (Springer Publ., Cham, 2019)
B. Rhodes, J. Mahaffey, J. Cannady, Multiple self-organization maps for intrusion detection, in Proceedings of the 23rd National Information Security Conference, 2000, pp. 32–42
O. Depren, M. Topallar, E. Anarim, M.K. Ciliz, An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl. 29, 713–722 (2005)
V. Veeramreddy, V.V. Rama Prasad, K.M. Prasad, A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28(7), 26–35 (2011)
T. Kohonen, Self-Organizing-Map (Springer Publ., Berlin, 2001)
C. Modi, A survey of intrusion detection techniques in cloud. J. Netw. Comput. Appl. 36(1), 42–57 (2013)
R. Mitchell, I.R. Chen, A survey of intrusion detection techniques for cyber-physical systems. ACM Comput. Surv. 46(4), 1–55 (2014)
S. Shieh, V. Gligor, A pattern-oriented intrusion detection model and its applications, in Proceedings of the Symposium on Security and Privacy, 1991, pp. 327–342
A. Denning, An intrusion detection model. IEEE Trans. Softw. Eng. 13, 222–232 (1967)
S.E. Smaha, Tools for misuse detection, in Proceedings of the International Social Security Association, 1993, pp. 711–716
T.F. Lunt, A. Tamaru, F. Gilham, R. Jagannathan, C. Jalali, P.G. Neumann, H.S. Javitz, A. Valdes, T.D. Garvey, A Real-Time Intrusion Detection Expert System (IDES), Final Technical Report SRI Project 6784, Contract No. N0003S89-C-0050, SRI Computer Science Laboratory, 1992
D.P.F. Möller, R.E. Haas, K.B. Akhilesh, Automotive electronics, IT, and cybersecurity, in Proceedings of the IEEE/EIT Conference, pp. 575–580
E. Karim, V.V. Proha, Cyber-physical systems security, in Applied Cyber-Physical Systems, ed. by S.S. Shuh, U.J. Tanik, J.N. Carbone, (Springer Publ., New York. 2014), pp. 75–84
C. Zimmer, B. Bhat, F. Mueller, S. Mohan, Time-based intrusion detection in applied cyber-physical systems, in Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, 2010, pp. 100–118
K. Scarfone, P. Mell, Guide to Intrusion Detection and Prevention Systems (National Institute of Standards and Technology (NIST), Gaithersburg, MD, 2007), pp. 800–894
A. Abdel-Aziz, Intrusion Detection and Response—Leveraging Next Generation FireWall Technology, SANS Institute Report, 2020
P. S. Krenke, A. Pal, A. Colaco (eds.), Proceedings or the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (Springer Publ., Cham, 2014)
K.L. Fox, R.R. Henning, J.H. Reed, R. Simonian, A neural network approach towards intrusion detection, in 13th National Computer Security Conference, 1990, pp. 125–134
O.Y. Al-Jarrah, O. Alhussein, P.D. Yao, S. Muhaidat, K. Taha, K. Kim, Data randomization and cluster-based partitioning for hotnet intrusion detection. IEEE Trans. Cybernet. 46, 1796–1806 (2015)
G. Kumar, Evaluation metrics for intrusion detection systems—a study. Int. J. Comput. Sci. Mobile Appl. 2(11), 11–17 (2014)
R. Caruana, A. Niculescu-Mizil, Data mining in metric space: an empirical analysis of supervised learning performance criteria, in Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, 2004, pp. 69–78
G. Gu, P. Fogla, D. Dragon, W. Lee, B. Scoric, Measuring intrusion detection capability: an information-theoretic approach, in Proceedings of the ACM Symposium on Information, Computer and Communications Security, 2006, pp. 90–101
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Möller, D.P.F. (2020). Intrusion Detection and Prevention. In: Cybersecurity in Digital Transformation. SpringerBriefs on Cyber Security Systems and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-60570-4_4
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DOI: https://doi.org/10.1007/978-3-030-60570-4_4
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