NILESH KUNHARE
Articles written in Sadhana
Volume 45 All articles Published: 7 May 2020 Article ID 0109
Particle swarm optimization and feature selection for intrusion detection system
NILESH KUNHARE RITU TIWARI JOYDIP DHAR
The network traffic in the intrusion detection system (IDS) has unpredictable behaviour due to the high computational power. The complexity of the system increases; thus, it is required to investigate the enormous number of features. However, the features that are inappropriate and (or) have some noisy dataseverely affect the performance of the IDSs. In this study, we have performed feature selection (FS) through a random forest algorithm for reducing irrelevant attributes. It makes the underlying task of intrusion detection effective and efficient. Later, a comparative study is carried through applying different classifiers, e.g., k Nearest Neighbour (k-NN), Support Vector Machine (SVM), Logistic Regression (LR), decision tree (DT) and Naive Bayes (NB) for measuring the different IDS metrics. The particle swarm optimization (PSO) algorithm wasapplied on the selective features of the NSL-KDD dataset, which cut down the false alarm rate and enhanced the detection rate and the accuracy of the IDS as compared with the mentioned state-of-the-art classifiers. This studyincludes the accuracy, precision, false-positive rate and the detection rate as performance metrics for the IDSs. The experimental results show low computational complexity, 99.32% efficiency and 99.26% detection rate onthe selected features (=10) out of a complete set (= 41).
Volume 45, 2020
All articles
Continuous Article Publishing mode
Click here for Editorial Note on CAP Mode
© 2021-2022 Indian Academy of Sciences, Bengaluru.