Abstract
Intrusion detection system (IDS) consists of set of techniques and methods for collection of packets from host system or network and analyzes those packets for anomalous content. IDSs mainly fall into two categories: signature-based IDSs and anomaly detection systems. A rule-based IDS compares the incoming packets against rule set in order to detect intrusion. A common approach followed is to build rule trees or finite automata with rule set and traverse it using a packet as input string. 30–60 % of total signature-based IDS processing time is spent on pattern matching [1]. The existing signature-based IDS cannot meet the speed demands imposed by both high network speeds and increasing number of signatures, and more CPU time is spent on searching for rules that match each packet. In this paper, we are going to present an analysis on IDS that is combined with other methods and techniques to produce greater results and hence contribute to the improvement of IDS.
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Acknowledgements
This project is financially supported by DRDO sponsored project titled Machine Learning Techniques for Data Mining-Based Intrusion Detection Systems (Ref. No.: ERIPR/ER/0705066/M/01/1256) to Dr. Srinivasa K G, Professor, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India. We acknowledge Dr. T V Suresh Kumar, Dr. K Rajanikanth, Dr. D E Geetha, Mrs. Mrunalini M, and Mr. Manish Kumar for their kind support.
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Manjunath, M., Srinivasa, K.G., Muppalla, A.K. (2014). Fast Pattern Matching Approach for Intrusion Detection Systems. In: Sridhar, V., Sheshadri, H., Padma, M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1157-0_39
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DOI: https://doi.org/10.1007/978-81-322-1157-0_39
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