An Evolutionary Computation Based Classification Model for Network Intrusion Detection
| Autor: | Manas Ranjan Patra, Ashalata Panigrahi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Computer science
business.industry Artificial immune system Best-first search Genetic programming Feature selection Intrusion detection system computer.software_genre Machine learning Evolutionary computation Clonal selection algorithm Information gain ratio Artificial intelligence Data mining business computer |
| Zdroj: | Distributed Computing and Internet Technology ISBN: 9783319149769 ICDCIT |
| DOI: | 10.1007/978-3-319-14977-6_31 |
| Popis: | Current techniques used for network intrusion detection have limited capabilities in coping with the dynamic and increasingly complex nature of security threats. In this paper, we propose a classification model for detecting intrusions based on Genetic Programming, Artificial Immune Recognition Systems AIRS1, AIRS2, and Clonal Selection Algorithm CLONALG. Further, six Rank based, viz., Information Gain, Gain ratio, Symmetrical Uncertainty, Chi squared Attribute Evaluator, Relief-F, and one-R; and five search based feature selection methods, viz., PSO Search, Genetic Search, Best First Search, Greedy Stepwise, and Rank Search have been employed to select the most relevant attributes before classification. The performance of the model has been evaluated in terms of accuracy, precision, detection rate, F-value, false alarm rate, and fitness value. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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