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A Feature Reduced Intrusion Detection System with Optimized SVM Using Big Bang Big Crunch Optimization

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Abstract

The swift proliferation in traffic across computer networks has led to certain types of attacks and intrusions, raising a serious global concern of information security. Attack detection is possible by monitoring and observing occurrences in intrusion detection systems, however these systems tend to suffer from problem of curse of dimensionality, high false alarm rate, high time complexity and low detections. In order to overcome these limitations, we propose a feature reduced intrusion detection system employing optimized SVM as a classifier. Feature Reduction has been performed by fusing ranked features from information gain and chi square in a way that it has helped in retaining only important features and discarding the rest. The study further proposes an optimized version of SVM classifier using Big Bang Big Crunch (BBBC) optimization that simulates the big bang and big crunch theory of evolution of universe. BBBC has helped in finding an optimal set of SVM parameters quickly that are further used for classification. We also experimented with a number of fitness functions for gauging the performance of IDS and propose a new fitness function based on the weighted F1 score of various traffic classes. KDD-99 dataset has been used for experimentation and analysis. The paper further experiments the effects of under-sampling and oversampling of various traffic classes on the proposed IDS performance and recommends that maintaining a desired ratio for a mix of under-sampling and over-sampling of desired classes produces the best results.

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Authors and Affiliations

  1. UIET, Panjab University Chandigarh, Chandigarh, India

    Mohit Nagpal &  Akashdeep Sharma

  2. CSED, Thapar Institute of Engineering and Technology, Derabassi Campus, Punjab, India

    Manisha Kaushal

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  1. Mohit Nagpal
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  2. Manisha Kaushal
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  3. Akashdeep Sharma
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All authors contributed towards planning, literature survey, experimentations, manuscript writing, editing and proof-reading.

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Correspondence to Akashdeep Sharma.

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Nagpal, M., Kaushal, M. & Akashdeep Sharma A Feature Reduced Intrusion Detection System with Optimized SVM Using Big Bang Big Crunch Optimization. Wireless Pers Commun 122, 1939–1965 (2022). https://doi.org/10.1007/s11277-021-08975-2

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