Genetic Algorithm Operations Implementation on Traffic Anomaly Intrusion Detection using NSLKDD Dataset
Nagarajan Munusamy1, L.G nanaprasanambikai2
1Dr. Nagarajan Munusamy, HOD & Associate Professor, K.S.G College of Arts & Science, Bharathiar University, Coimbatore, (Tamil Nadu), India.
2L.Gnanaprasanambikai, Assistant Professor, Nehru Arts and Science College, Bharathiar University, Coimbatore, (Tamil Nadu), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1228-1232 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6960068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intrusion Detection is one of the security tools in Network Security. Anomaly Intrusion Detection is a method of Intrusion Detection to find novel attacks by using decision rules. These decision rules need to be globally fit solution to find new attacks. Genetic Algorithm is a evolutionary search algorithm, which gives the globally fit solutions in the state space search. Genetic Algorithm process success depends on its fitness function. In this paper, a suitable fitness function is proposed to find global fit rules, for Traffic anomaly intrusion detection. For a better Intrusion Detection Performance the discussion of proposed fitness function results with test data and comparison with existing fitness function are done and tabulated.
Keyword: Fitness Function, Genetic Algorithm, GA-Operations, Anomaly Intrusion Detection.
Scope of the Article: Web Algorithms.