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Visumathi, J.
- An Efficient Intrusion Detection System Using Computational Intelligence
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National Journal of System and Information Technology, Vol 3, No 2 (2010), Pagination: 117-126Abstract
Intrusion detection system is one of the widely used tools for defense in Computer Networks. In literature, plenty of research is published on Intrusion Detection Systems. In this paper we present a survey of Intrusion Detection Systems. We survey the existing types, techniques and approaches of Intrusion Detection Systems in the literature. Finally we propose a new architecture for Intrusion Detection System and outline the present research challenges and issues in Intrusion Detection SystemKeywords
Intrusion Detection, Neural Network, Fuzzy logic, Artificial Intelligence, Honeypot, Data Mining
References
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- Improved Detection of Dos Attacks Using Intelligent Computation Techniques
Abstract Views :243 |
PDF Views:4
Authors
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National Journal of System and Information Technology, Vol 3, No 2 (2010), Pagination: 127-138Abstract
IDSs play a principal role in pro-actively detecting intrusions into enterprise-level computer networks, therefore the accuracy with which it performs this vital function is of paramount importance. Many studies have previously been conducted to improve upon proper classification of detections using neural networks and machine learning algorithms. We try to compare the performance of various intelligent computation techniques like Bayesian networks, Naive Bayesian, Logistic regression, RBF networks, Multi-Layer perception, SVMs with the SMO model, Kth nearest neighbour and Random forest in detecting DoS attack patterns. The data that was used to train and validate these techniques was obtained from the MIT Lincoln lab study into IDSs. The results obtained provide a clear comparison of the individual intelligent computation techniques ability in identifying and classifying attack patterns.Keywords
Networks, Intrusion Detection, Denial of Service, Datasets, Data Mining, Bayesian Networks, Naive Bayesian, Logistic Regression, RBF Networks, Multi-layer Perception, Support Vector Machines, Sequential Minimal Optimization, Kth Nearest Neighbor, Random ForestReferences
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