Of late, research works on Intrusion Detection System have been receiving a lot of attention. An IDS detects hazard patterns of network traffic on the residual open parts through observing user activities [1]. There are several models available as of now, but the major loop hole in most of the existing models is the incapability of cognizing new attacks i.e. novel threats to a system. Anomaly based intrusion detection system has undoubtedly resulted in easing the pain of detecting novel threats for a system when compared to its counterpart, Signature based Intrusion Detection System. This paper gives an overview of various Anomaly Intrusion Detection System techniques like machine learning algorithms, data mining methods and its variants e.g. Entropy data mining, neural network methods etc. We also give an overview of a few hybrid techniques that have been employed and have resulted in better outcomes for e.g. a combination of Neural networks and Fuzzy logic method.

1. Muna M., Taher Jawhar and Monica Mehrotra” Anomaly Intrusion Detection System using Hamming Network Approach” International Journal of Computer Science & Communication, Vol. 1, No. 1, pp. 165-169, January-June, 2010.
2. Dewan Md. Farid and Mohammad Zahidur Rahman, “Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm”, Journal of Computers, Vol.5, No.1, January, 2010.
3. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., and Stolfo, S. J., “A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data”, Applications of Data Mining in Computer Security, Kluwer Academic Publishers, pp. 78-99, 2002.
4. W. Lee, S. Stolfo, and K. Mok, “A Data Mining Framework for Building Intrusion Detection Model”, In Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA: IEEE Computer Society Press, pp. 120-132, 1999.
5. Mathew V Mohoney and Philip K Chan, “Learning Non Stationary Models of Normal Network Traffic for detecting Novel Attacks” Proc. Eighth Int1. Conf. Knowledge Discovery and Data Mining, p376-385, 2002.
6. Wenke Lee and Salvatore J. Stolfo, “Data Mining Approaches for Intrusion Detection”, Proceedings of the 7th USENIX Security Symposium, San Antonio, Texas, January 26-29, 1998.
7. W. Lee, S. Stolfo, P. Chan, E. Eskin, W. Fan, M. Miller, S. Hershkop, and J. Zhang, “Real Time Data Mining-Based Intrusion Detection”, In DARPA Information Survivability Conference and Exposition II, June 2001.
8. K.Yoshida, “Entropy Based Intrusion Detection”, In Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and signal Processing, Vol. 2, pp. 840 – 843, Aug 28- 30 ,2003
9. Cannady J, “Artificial Neural Networks for Misuse Detection”, In Proceedings of the ’98 National Information System Security Conference (NISSC’98), pp. 443-456, 1998.
10. Marjan Bahrololum, Elham Salahi, and Mahmoud Khaleghi, “An Improved Intrusion Detection Technique based on two Strategies Using Decision Tree and Neural Network”, Vol. 4, No. 4, pp. 96-101, 2009.
11. K.S. Anil Kumar and Dr. V. NandaMohan, "Novel Anomaly Intrusion Detection Using Neuro-Fuzzy Inference System", IJCSNS International Journal 6 of Computer Science and Network Security, vol.8, no.8, pp.6-11 , August 2008.

Intrusion detection Data mining Anomaly intrusion detection Neural Networks fuzzy logic