Whether we realize it or not, machine learning touches our daily lives in many ways. When you upload a picture on social media, for example, you might be prompted to tag other people in the photo. That’s called image recognition, a machine learning capability by which the computer learns to identify facial features. Other examples include number and voice recognition applications. From an intrusion detection perspective, analysts can apply machine learning, data mining and pattern recognition algorithms to distinguish between normal and malicious traffic. One way that a computer can learn is by examples. With the advances in information technology (IT) criminals are using cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly vulnerable to intrusions and other threats. Physical devices and human intervention are not sufficient for monitoring and protection of these infrastructures; hence, there is a need for more sophisticated cyber defense systems that need to be flexible, adaptable and robust, and able to detect a wide variety of threats and make intelligent real-time decisions. Numerous bio-inspired computing methods of Machine Learning have been increasingly playing an important role in cyber crime detection and prevention. The purpose of this study is to present advances made so far in the field of applying ML techniques for combating cyber crimes, to demonstrate how these techniques can be an effective tool for detection and prevention of cyber attacks, as well as to give the scope for future work.

Intrusion Detection; Machine Learning

[1] S. Singh and S. Silakari, "A Survey of Cyber Attack Detection Systems", IJCSNS International Journal of Computer Science and Network Security, vol. 9, no. 5, 2009 [Online].Available:http://paper.ijcsns.org/07_book/200905/20090501.pdf. [Accessed: 08- Feb- 2016]
[2] S. Simmons, D. Edwards, N. Wilde, J. Just and M. Satyanarayana, "Preventing Unauthorized Islanding: Cyber-Threat Analysis", 2006 IEEE/SMC International Conference on System of Systems Engineering, pp. 5, 24-26 [Online]. Available:http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=165229&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1652294. [Accessed: 11- Feb- 2016]
[3] I. Ionita and L. Ionita, "An agent-based approach for building an intrusion detection system",RoEduNet International Conference 12th Edition: Networking in Education and Research, pp. 1-6, 26-28, 2013 [Online]. Available: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6714184. [Accessed: 11- Feb- 2016]
[4] S. Dilek, H. Çakır and M. Aydin, “APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO COMBATING CYBER CRIMES: A REVIEW", International Journal of Artificial Intelligence & Applications (IJAIA), vol. 6, no. 1, 2015 [Online]. Available: http://arxiv.org/ftp/arxiv/papers/1502/1502.03552.pdf. [Accessed: 13- Feb- 2016]
[5] A. Cerli and D. Ramamoorthy, "Intrusion Detection System by Combining Fuzzy Logic with Genetic Algorithm", Global Journal of Pure and Applied Mathematics (GJPAM), vol. 11, no. 1, 2015 [Online]. Available: http://ripublication.com/gjpamspl/gjpamv11n1spl_20.pdf. [Accessed: 09- Feb- 2016]
[6] F. Rosenblatt. The Perceptron -- a perceiving and recognizing automaton. Report 85- 460-1, Cornell Aeronautical Laboratory, 1957.
[7] G. Klein, A. Ojamaa, P. Grigorenko, M. Jahnke, E. Tyugu. Enhancing Response Selection in Impact Estimation Approaches. Military Communications and Information Systems Conference (MCC), Wroclaw, Poland, 2010.
[8] http://en.wikipedia.org/wiki/Expert_system. Expert System. Wikipedia.
[9] J. Kivimaa, A. Ojamaa, E. Tyugu. Graded Security Expert System. Lecture Notes in Computer Science, v. 5508. Springer, 2009, 279-286.
[10] D. Anderson, T. Frivold, A. Valdes. Next- generation intrusion detection expert system (NIDES). Technical Report SRI-CSL-95-07, SRI International, Computer Science Lab (1995).
[11] TF. Lunt, R. Jagannathan. A Prototype Real-Time Intrusion-Detection Expert System. Proc. IEEE Symposium on Security and Privacy, 1988, p. 59.
[12] L. Rui, L. Wanbo, (2010) “Intrusion Response Model based on AIS”, International Forum on Information Technology and Applications (IFITA), Vol. 1, pp. 86 – 90.
[13] U. Kaster, B. Kuhiber. Information and Knowledge Management in C2 Systems – The Gap Between Theory and Practice is not all that big. In: M.- Amanovicz. Comcepts and Implementations for Innovative Military Communications and Information Technologies. Military University of Technology Publisher, Warsaw, 2010, pp. 98 – 107.
[14] J. Kaster. Combined Knowledge Management and Workflow Management in C2 Systems – a user centered approach. Fraunhofer Institute for Communication, Information Processing and Ergonomics. Report ID # 197, 2009.
[15] http://singinst.org/overview/whatisthesingularity/
[17] R. Kurtzwell. The Singularity is Near. Viking Adult. 2005.
[18]http://www.ted.com/webcast/archive/event/ibmwatson
[19] J. Kivimaa, A. Ojamaa, E. Tyugu. Pareto-Optimal Situation Analysis for Selection of Security Measures. Proc. MilCom, 2008.