Intelligent Network Management and Control. Badr Benmammar

Чтение книги онлайн.

СКАЧАТЬ Unsupervised anomaly detection in network intrusion detection using clusters. Proceedings of the 28th Australasian Conference on Computer Science. Australian Computer Society Inc., Darlinghurst, 333–342.

Li, L., De-Zhang, Y. and Chen, F.-S. (2010). A novel rule-based Intrusion Detection System using data mining. 3rd International Conference on Computer Science and Information Technology. IEEE, Chengdu.

      Liang, J. et al. (2019). A filter model for intrusion detection system in vehicle ad hoc networks: A hidden Markov methodology. Knowledge-Based Systems, 163, 611–623.

      Liao, Y. and Vemuri, V.R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, 21(5), 439–448.

      Lippmann, R.P. and Cunningham, R.K. (2000). Improving intrusion detection performance using keyword selection and neural networks. Computer Networks, 34(4), 597–603.

      Lunt, T. (1993). Detecting intruders in computer systems. Proceedings of the 1993 Conference on Auditing and Computer Technology. Baltimore Convention Center, Baltimore.

      Lunt, T.F. (1990). Real-time intrusion detection expert system. Computer Science Lab., SRI International, Technical Report.

      Mahoney, M.V. and Chan, P.K. (2001). PHAD: Packet header anomaly detection for identifying hostile network traffic [Online]. Available at: https://pdfs.semanticscholar.org/1505/f3658f5af7dff88e88d6a2b381de12e03036.pdf.

      Mahoney, M.V. and Chan, P.K. (2002a). Learning models of network traffic for detecting novel attacks. Technical Report, Florida Institute of Technology, Melbourne.

      Mahoney, M.V. and Chan, P.K. (2002b). Learning nonstationary models of normal network traffic for detecting novel attacks. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Edmonton.

      Menahem, E., Shabtai, A., Rokach, L. and Elovici, Y. (2009). Improving malware detection by applying multi-inducer ensemble. Computational Statistics & Data Analysis, 53(4), 1483–1494.

      Miles, B., Shahar, A., Jack, C., Helen, T., Peter, E., Ben, G., Allan, D., Paul, S., Thomas, Z., Bobby, F., Hyrum, A., Heather, R., Gregory, C.A., Jacob, S., Carrick, F., Seán, Ó. h., Simon, B., Haydn, B., Sebastian, F., Clare, L., Rebecca, C., Owain, E., Michael, P., Joanna, B., Roman, Y. and Dario, A. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation [Online]. Available at: https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf.

      Mishra, A., Agrawal, A. and Ranjan, R. (2011). Artificial intelligent firewall. Proceedings of the International Conference on Advances in Computing and Artificial Intelligence. ACM, Rajpura/Punjab.

      Moon, D., Im, H., Kim, I. and Park, J. H. (2017). DTB-IDS: An intrusion detection system based on decision tree using behavior analysis for preventing APT attacks. The Journal of Supercomputing, 73(7), 2881–2895.

      Moore, T. and Anderson, R. (2012). Internet Security. The Oxford Handbook of the Digital Economy. Oxford University Press, Oxford.

Mukkamala, S. and Sung, A.H. (2003a). Artificial intelligent techniques for intrusion detection. International Conference on Systems, Man and Cybernetics. IEEE, Washington.

      Mukkamala, S. and Sung, A.H. (2003b). A comparative study of techniques for intrusion detection. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’03). IEEE, Washington.

      Mukkamala, S., Sung, A.H., and Abraham, A. (2005). Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications, 28, 167–182.

      Mutz, D., Robertson, W., Vigna, G., and Kemmerer, R. (2007). Exploiting execution context for the detection of anomalous system calls. Proceedings of the International Symposium on Recent Advances in Intrusion Detection. RAID, Gold Coast.

      Novikov, D., Yampolskiy, R.V., and Reznik, L. (2006). Artificial intelligence approaches for intrusion detection. IEEE Long Island Systems, Applications and Technology Conference. IEEE, Long Island.

      Peltier, T.R. (2010). Information Security Risk Analysis. CRC Press, Boca Raton.

      Peng, K., Leung, V., Zheng, L., Wang, S., Huang, C., and Lin, T. (2018). Intrusion detection system based on decision tree over big data in fog environment [Online]. Available at: https://www.hindawi.com/journals/wcmc/2018/ 4680867/.

      Ponkarthika, M. and Saraswathy, V.R. (2018). Network intrusion detection using deep neural networks. Asian Journal of Applied Sciences, 2(2), 665–673.

      Quamar, N., Weiqing, S., Ahmad, Y.J., and Mansoor, A. (2016). A deep learning approach for network intrusion detection system. Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ICST publisher, December 3–5, 2015, New York, USA, 21–26.

      Rai, K., Devi, M.S., and Guleria, A. (2016). Decision tree based algorithm for intrusion detection. International Journal of Advanced Networking and Applications, 7(4), 2828.

      Rawat, S. (2005). Efficient data mining algorithms for intrusion detection. Proceedings of the 4th Conference on Engineering of Intelligent Systems (EIS 2004). EIS, Madeira.

      Robertson, W., Maggi, F., Kruegel, C., and Vigna, G. (2010). Effective anomaly detection with scarce training data. Proceedings of the Network and Distributed System Security Symposium, NDSS, San Diego.

      Roesch, M. (1999). Snort: Lightweight intrusion detection for networks. Lisa, 99(1), 229–238.

      Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1/2), 1–39.

      Sabhnani, M. and Serpen, G. (2003). Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context. International Conference on Machine Learning; Models, Technologies and Applications. MLMTA, Las Vegas.

Sahu, S. and Mehtre, B.M. (2015). Network intrusion detection system using J48 Decision Tree. International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, Kochi.

      Sai Satyanarayana Reddy, S., Chatterjee, P., and Mamatha, C. (2019). Intrusion detection in wireless network using fuzzy logic implemented with genetic algorithm. In Computing and Network Sustainability, Peng, S.-L, Dey, N., and Bundele, M. (eds). Springer, Berlin, 425–432.

      Scharre, P. (2015). Counter-swarm: A guide to defeating robotic swarms [Online]. Available at: https://warontherocks.com/2015/03/counter-swarm-a-guide-todefeating-robotic-swarms/.

      Schneier, B. (2008). The psychology of security. International Conference on Cryptology in Africa. AFRICACRYPT, Casablanca.

      Shanmugavadivu, R. and Nagarajan, N. (2011). Network intrusion detection system using fuzzy logic. Indian Journal of Computer Science and Engineering, 2(1), 101–111.

      Szegedy, C., Zaremba, W., Sutskever, СКАЧАТЬ