An intrusion detection technique based on continuous binary communication channels

Choosing Intrusion Detection Systems (IDSs) is difficult owing to the availability of different IDS techniques, the nature of attacks and the inability of IDS techniques to guarantee total security. The use of Intrusion Prevention Systems (IPSs) as a first line of defence does not prevent all attacks, so IDSs are needed as a second line of defence. Anomaly and signature-based techniques are the main approaches employed by IDSs. This new IDS technique is based on two main anomaly detection techniques: statistical and predictive pattern generation techniques. This IDS will detect both known and unknown attacks using signature-based and anomaly techniques.

[1]  Malcolm I. Heywood,et al.  On dataset biases in a learning system with minimum a priori information for intrusion detection , 2004, Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004..

[2]  Annamalai,et al.  An Intrusion Detection Technique Based on Discrete Binary Communication Channels , 2011 .

[3]  Qinghua Sun,et al.  The fractal feature of telecommunication network , 2003, International Conference on Communication Technology Proceedings, 2003. ICCT 2003..

[4]  Jizhou Sun,et al.  Honeypot and scan detection in intrusion detection system , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[5]  C. M. Akujuobi,et al.  Enterprise network intrusion detection and prevention system (ENIDPS) , 2007, SPIE Defense + Commercial Sensing.

[6]  Some results on the self-similarity property in communication networks , 2004, IEEE Transactions on Communications.

[7]  Yong Sheng,et al.  A parallel decision tree-based method for user authentication based on keystroke patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  M.N.O. Sadiku,et al.  Application of Signal Detection and Estimation Theory to Network Security , 2007, 2007 IEEE International Symposium on Consumer Electronics.

[9]  Qi Zhang,et al.  Indra: a peer-to-peer approach to network intrusion detection and prevention , 2003, WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003..

[10]  R. Hixon,et al.  Markov chains in network intrusion detection , 2004, Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, 2004..

[11]  Xing Li,et al.  Wavelet based data mining and querying in network security databases , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[12]  Taghi M. Khoshgoftaar,et al.  Resource-sensitive intrusion detection models for network traffic , 2004, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings..

[13]  M.N.O. Sadiku,et al.  Application of Wavelets and Self-similarity to Enterprise Network Intrusion Detection and Prevention Systems , 2007, 2007 IEEE International Symposium on Consumer Electronics.

[14]  Walter Willinger,et al.  Self-similar traffic and network dynamics , 2002, Proc. IEEE.

[15]  Lucas M. Venter,et al.  A comparison of Intrusion Detection systems , 2001, Comput. Secur..

[16]  Jianping Wu,et al.  Wavelet-based analysis of network security databases , 2003, International Conference on Communication Technology Proceedings, 2003. ICCT 2003..