Читать книгу Intelligent Network Management and Control - Badr Benmammar - Страница 33

1.6. References

Оглавление

Agarwal, R. and Joshi, M.V. (2000). A new framework for learning classifier models in data mining [Online]. Available at: https://pdfs.semanticscholar.org/db6e/1d67f7912efa65f94807dc81b24dea2de158.pdf [Accessed January 2019].

Ahlan, A.R., Lubis, M., and Lubis, A.R. (2015). Information security awareness at the knowledge-based institution: Its antecedents and measures. Procedia Computer Science (PCS). 72(2015), 361–373.

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané. (2016). Concrete problems in AI safety [Online]. Cornell University. Available at: https://arxiv.org/abs/1606.06565.

Anderson, D., Frivold, T., and Valdes, A. (1995). Next-generation intrusion detection expert system (NIDES). Report, US Department of the Navy, Space and Naval Warfare Systems Command, San Diego.

Aslahi-Shahri, B.M., Rahmani, R., Chizari, M., Maralani, A., Eslami, M., Golkar, M.J., and Ebrahimi, A. (2016). A hybrid method consisting of GA and SVM for intrusion detection system. Neural Computing and Applications, 27(6), 1669–1676.

Bace, R.G. (2000). Intrusion Detection. Sams Publishing, Indianapolis.

Balan, E.V., Priyan, M.K., Gokulnath, C., and Devi, G.U. (2015). Fuzzy based intrusion detection systems in MANET. Procedia Computer Science, 50, 109–114.

Barth, C.J. and Mitchell, J.C. (2008). Robust defenses for cross-site request forgery. Proceedings of 15th ACM Conference. CCS, Alexandria.

Biggio, B., Nelson, B., and Laskov, P. (2012). Poisoning attacks against support vector machines. 29th International Conference on Machine Learning. ICML, Edinburgh, 1467–1474.

Capgemini Research Institute (2019). Reinventing cybersecurity with artificial intelligence: The new frontier in digital security [Online]. Available at: https://www.capgemini.com/wp-content/uploads/2019/07/AI-in-Cybersecurity_Report_2019 0711_V06.pdf.

Chebrolu, S., Abraham, A., and Thomas. (2005). Feature deduction and ensemble design of intrusion detection systems. Computers & Security, 24(4), 295–307.

Chen, W.-H., Hsu, S.-H., and Shen, H.-P. (2005). Application of SVM and ANN for intrusion detection. Computers & Operations Research, 32(10), 2617–2634.

Cova, M., Balzarotti, D., Felmetsger, V., and Vigna, G. (2007). Swaddler: An approach for the anomaly-based detection of state violations in web applications. Proceedings of the 10th International Symposium on Recent Advances in Intrusion Detection. RAID, Gold Coast.

Cova, M., Kruegel, C., and Vigna, G. (2010). Detection and analysis of drive-by-download attacks and malicious JavaScript code. Proceedings of the 19th International Conference on the World Wide Web. WWW, Raleigh.

Crockford, D. (2015). Json [Online]. Available at: https://github.com/douglascrockford/JSON-js/blob/master/README [Accessed March 2018].

Cunningham, R. and Lippmann, R. (2000). Detecting computer attackers: Recognizing patterns of malicious stealthy behavior. Presentation, CERIAS, Anderlecht.

Ertoz, L., Eilertson, E., Lazarevic, A., Tan, P.N., Kumar, V., Srivastava, J., & Dokas, P. (2004). Minds-Minnesota intrusion detection system. Next Generation Data Mining, August, 199–218.

Fortuna, C., Fortuna, B., and Mohorčič, M. (2002). Anomaly detection in computer networks using linear SVMs [Online]. Available at: http://ailab.ijs.si/dunja/SiKDD2007/Papers/Fortuna_Anomaly.pdf.

Hajimirzaei, B. and Navimipour, N.J. (2019). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express, 5(1), 56–59.

Hamamoto, A.H., Carvalho, L.F., Sampaio, L.D.H., Abrão, T., & Proença Jr, M.L. (2018). Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Systems with Applications, 92, 390–402.

Han, X., Xu, L., Ren, M., and Gu, W. (2015). A Naive Bayesian network intrusion detection algorithm based on principal component analysis. 7th International Conference on Information Technology in Medicine and Education. IEEE, Huangshan.

Heckerman, D. (2008). A tutorial on learning with Bayesian networks. Innovations in Bayesian Networks, Holmes, D.E. and Jain, L.C. (eds). Springer, Berlin, 33–82.

Hoque, M.S. et al. (2012). An implementation of intrusion detection system using genetic algorithm. International Journal of Network Security & Its Applications (IJNSA). AIRCC publisher, 4(2), 109–120.

Hu, J., Yu, X., Qiu, D., and Chen, H.H. (2009). A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection. IEEE Network, 23(1), 42–47.

Hwang, T.S., Lee, T.-J., and Lee, Y.-J. (2007). A three-tier IDS via data mining approach. Proceedings of the 3rd Annual ACM Workshop on Mining Network Data. ACM, San Diego.

Idris, N.B. and Shanmugam, B. (2005). Artificial intelligence techniques applied to intrusion detection. Annual IEEE India Conference (Indicon). IEEE, Chennai.

Ingham, K., Somayaji, A., Burge, J., and Forrest, S. (2007). Learning DFA representations of HTTP for protecting web applications. Journal of Computer Networks, 51(5), 1239–1255.

Kalaivani, S., Vikram, A., and Gopinath, G. (2019). An effective swarm optimization based intrusion detection classifier system for cloud computing. 5th International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, Coimbatore.

Kang, M.-J. and Kang, J.-W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PLOS ONE, 11(6), 1–17.

Khidzir, N.Z., Daud, K.A.M., Ismail, A.R., Ghani, M.S.A.A., and Ibrahim, M.A.H. (2018). Information Security Requirement: The Relationship Between Cybersecurity Risk Confidentiality, Integrity and Availability in Digital Social Media. Regional Conference on Science, Technology and Social Sciences (RCSTSS 2016). 4–6 December 2016, Penang, Malaysia.

Kim, J., Kim, J., Thu, H.L.T., and Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. International Conference on Platform Technology and Service (PlatCon). IEEE, Jeju.

Kirdaa, E., Jovanovicb, N., Kruegelc, C., and Vigna, G. (2009). Client-side cross-site scripting protection. Computers & Security, 28(7), 592–604.

Kruegel, C., Mutz, D., Robertson, W., and Valeur, F. (2003). Bayesian event classification for intrusion detection. Proceedings of the 19th Annual Computer Security Applications Conference. IEEE, Las Vegas.

Kudłacik, P., Porwik, P., and Wesołowski, T. (2016). Fuzzy approach for intrusion detection based on user’s commands. Soft Computing, 20(7), 2705–2719.

Kumar, S., Krishna, C.R., and Solanki, A.K. (2018). A technique to resolve data integrity and confidentiality issues in a wireless sensor network. 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, Noida.

Landwehr, C. (2008). Cybersecurity and artificial intelligence: From fixing the plumbing to smart water. IEEE, Security and Privacy, 6(5), 3–4.

Leung, K. and Leckie, C. (2005). 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, I., Bruna, J., Erhan, D., Goodfellow, I. and Fergus, R. (2013). Intriguing properties of neural networks [Online]. Available at: https://arxiv.org/abs/1312.6199.

Tekerek, A. and Bay, O.F. (2019). Design and implementation of an artificial intelligence-based web application firewall model. Neural Network World, 189, 206.

Teng, H.S. and Chen, K. (1990). Adaptive real-time anomaly detection using inductively generated sequential patterns. Proceedings of the 1990 IEEE Computer Society Symposium on Research in Security and Privacy. IEEE, Oakland.

Turner, C., Jeremiah, R., Richards, D., and Joseph, A. (2016). A rule status monitoring algorithm for rule-based intrusion detection and prevention systems. Procedia Computer Science, 95, 361–368.

Valentín, K. and Malý, M. (2014). Network firewall using artificial neural networks. Computing and Informatics, 32(6), 1312–1327.

Vapnik, V. (1998). Statistical Learning Theory. John Wiley and Sons, Hoboken.

Veiga, A.P. (2018). Applications of artificial intelligence to network security [Online]. Available at: https://arxiv.org/abs/1803.09992.

Vinayakumar, R., Soman, K.P. and Poornachandran, P. (2017). Applying convolutional neural network for network intrusion detection. 6th International Conference on Advances in Computing, Communications and Informatics (ICACCI). Manipal University, Karnataka.

Witten, I.H. and Frank, E. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington.

Yang, Y., McLaughlin, K., Littler, T., Sezer, S. and Wang, H.F. (2013). Rule-based intrusion detection system for SCADA networks. 2nd IET Renewable Power Generation Conference (RPG 2013). RPG, Beijing.

Zainal, A., Maarof, M.A. and Shamsuddin, S.M. (2009). Ensemble classifiers for network intrusion detection system. Journal of Information Assurance and Security, 4(3), 217–225.

Zegeye, W.K., Moazzami, F. and Dean, R. (2018). Hidden Markov Model (HMM) based Intrusion Detection System (IDS). International Telemetering Conference Proceedings, 5–8 November 2018, Glendale, Arizona.

Intelligent Network Management and Control

Подняться наверх