Topological Analysis of Network Systems for Intrusion Detections
DOI:
https://doi.org/10.14738/tnc.42.1989Keywords:
Intrusion, conditional probability, network system, regression, data analysisAbstract
An understanding of how well networks will respond to ongoing attack threats is an important task in formulating strategies to protect unauthorized network activities. The study of topological properties of network architecture sheds some light in this effort. The purpose of this paper is to study several scenarios that address topological structures and related analyses of network systems to begin the appropriate discussion towards this question. Analysis of the probabilistic state finite automation and its probability distribution theory play a pivotal role in the discussion.References
(1) Kumar, V. Anil (2004). Sophisticated in Distributed Denial-of-Service Attacks on the Internet, Current Science, Vol. 87, No. 7, pp. 885-888
(2) CERT Insider Threat Center (2009). Software Engineering Institute, Carnegie Mellon University, Last updated February 12, 2009, http://www.cert.org/stats/
(3) Internet Crime Report, Internet Crime Complaint Center (2012). http://www.ic3.gov/media/annualreport/2011_ic3report.pdf
(4) Chang, H.-Y., Wu, S. F. and Jou, Y. F. (2001). Real-Time Protocol
Analysis for Detecting Link-State Routing Protocol Attacks, ACM Trans. Inf. Sys. Sec., Vol. 1, (2001), pp. 1-36
(5) Barbará, D., Couto, J., Jajodia, S., Popyack, L., and Wu, N. (2001). ADAM: Detecting Intrusions by Data Mining, Proceedings of the 2001 IEEE Workshop on Information Assurance and Security United States Military Academy, West point, NY, pp. 5—6, June 2001
(6) Li, Z., Gao, Y., and Chen, Y. (2005). Towards a High-speed Router-based Anomaly/Intrusion Detection System, Northwestern University, http://www.sigcomm.org/sigcomm2005/poster-121.pdf
(7) Sebyala, A. A., Olukemi, T., and Sacks, L., (2002). Active Platform Security through Intrusion Detection Using Naïve Bayesian Network for Anomaly Detection, http://www.ee.ucl.ac.uk/lcs/papers2002/LCS116.pdf
(8) Kang, D.-K., Fuller, D., and Honavar, V. (2005). Learning Classifiers for Misuse and Anomaly Detection Using a Bag of System Calls Representation, Proceedings of the 2005 IEEE Workshop on Information Assurance and Security United States Military Academy,
West Point, NY
(9) Yang, G., Zhou, W., and Qiao, D. (2007). Defending against barrier intrusions with mobile sensors, the Proceedings of 2007 International Conference on Wireless Algorithms, Systems and Applications, 2007, pp. 113-120
(10) Rafiee, M. and Bayen, A. M. (2010). Optimal network topology design in multi-agent systems for efficient average consensus, Decision and Control (CDC), 2010 49th IEEE Conference on, Atlanta, GA, 2010, pp. 3877-3883
(11) Barbosa, R. R. R. and Pras, A. (2010). Intrusion Detection in SCADA Networks, the Proceedings of the Conference: Mechanisms for Autonomous Management of Networks and Services, 4th International Conference on Autonomous Infrastructure, Management and Security, AIMS 2010, Zurich, Switzerland, June 23-25
(12) Schuster, F. and Paul, A. (2012). A distributed intrusion detection system for industrial automation networks," Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on, Krakow, 2012, pp. 1-4
(13) Freeman, S., Branch, J., Bivens, A., and Szymanski, B. (2002). Host-Based Intrusion Detection Using User Signatures, Proc. Research Conference, Troy, NY 12180-3590, http://www.cs.rpi.edu/~szymansk/papers/signature.pdf
(14) Petersson, K. M., Grenholm, P., and Forkstam, C. (2005). Artificial grammar learning and neural networks. In G. B. Bruna, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1726-1731
(15) Kermorvant, C. and Dupont, P. (2002). Stochastic grammatical inference with multinomial tests. In 6th International Colloquium on Grammatical Inference: Algorithms and Applications (ICGI), Vol. 2484 of Lecture Notes in Computer Science, Springer, 2002, pp. 149–160
(16) Abe, N. and Warmuth, M. K. (1990). On the Computational
Complexity of Approximating Distributions by Probabilistic Automata, Machine Learning, 1990, pp. 205-260
(17) Bose, K. Sanjay (2002). An Introduction to Queueing Systems Kluwer Academic/Plenum Publishers, New York, 2002
(18) Myers, J. L. and Well, A. D. (2003). Research Design and Statistical Analysis, second edition, 2003, Lawrence Erlbaum Associates, Inc. Publishers
(19) Wegman E. J. and Marchette, D. J. (2004). Statistical Analysis of Network Data for Cybersecurity, Chance, Vol. 17, No. 1 (2004), pp. 9-19
(20) Elbaum, S. and Munson, J. C. (1999). Intrusion Detection: Through Dynamic Software Measurement, Proceedings of the Workshop on Intrusion Detection and Network Monitoring, Santa Clara, California, USA, April 9—12, 1999, The USENIX Association, The Advanced
Computing Systems Association
(21) Boodnah, J. and Scharf, E. M. (2005). Applying Clustering to a Framework for Generating Trust, http://www.ctr.kcl.ac.uk/iwwan2005/papers/39.pdf
(22) Munson, J. C. and Elbaum, S. (1999). Software Reliability as a Function of User Execution Patterns, Proceedings of the 32nd Hawaii International Conference on System Sciences – 1999, pp. 1-12, http://cse.unl.edu/~elbaum/papers/workshops/hawai99.pdf
(23) Van Oorschot, P. C., Robert, J.-M., and Martin, M. V. (2006). A monitoring system for detecting repeated packets with applications to computer worms Int. J. Inf. Secur. (2006) 5(3): pp. 186–199
Holm, H. (2014). A large-scale study of the time required to compromise a computer system, Browse Journals & Magazines: Dependable and Secure Computing, IEEE Transactions, Vol. 11 Is. 1, 2014, pp. 2-15
