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Hiwarkar, Tryambak
- A Novel Approach for Feature Selection Technique in NSL-KDD Data Set of Cyber Security
Authors
1 SSSUTMS, Bhopal, M.P., IN
2 PVPSIT, Vijayawada, A.P., IN
Source
Indian Journal of Automation and Artificial Intelligence, Vol 6, No 6 (2019), Pagination: 1-5Abstract
Background/Objectives: To design the best and worst solution and also to minimise false alarm rate and maximize the detection rate by using AJO
Methods/Statistical analysis: The Adaptive Jaya Optimization (AJO) technique is used to select best features among 41 features.
Findings: The intrusion detection work focuses on feature selection, because few of the features are inappropriate and additional which results prolonged detection procedure and diminishes the performance of an intrusion detection system (IDS). With AJO technique best 17 features were selected to have best accuracy.
Application: In this study the NSL-KDD data set is analysed and applied Adaptive Jaya Technique for selecting best features to minimize low false alarm rate & maximize detection rate.
Keywords
Intrusion Detection System, NSL-KDD Dataset, Feature Selection, Adaptive Jaya Optimization.Full Text
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