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An Integrated IDS Using ICA-Based Feature Selection and SVM Classification Method

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Illumination of Artificial Intelligence in Cybersecurity and Forensics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 109))

Abstract

The continuous development of computer networks has created serious worries about vulnerability and security. Network administrators have embraced Intrusion Detection Systems (IDS) to offer vital network security. Commercial IDS in the market are incapable of detecting fresh threats and instead produce false alarms for the typical user activity. Artificial Intelligence (AI) may be used to address these difficulties and enhance accuracy. ICA-based feature selection (FS) ranks features based on the attribute-class label correlation. The authors suggested an ICA-based feature selection algorithm combined with a support vector machine (SVM) classifier for detecting anomalies in network connections. The KDDCUP 99 datasets, which is a benchmark dataset for intrusion detection with current threats, were used in the experiments. In contrast to several state-of-the-art approaches, the suggested model outperforms them in terms of accuracy, sensitivity, detection rate (DR) false alarm, and specificity. IDS may be used to secure wireless payment systems. It is possible to establish secure integrated network management that is error-free, therefore boosting performance.

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Author information

Authors and Affiliations

  1. Center of ICT/ICE Research, Covenant University, Ota, Nigeria

    Roseline Oluwaseun Ogundokun

  2. Department of Computer Science and Communication, Ostfild University College, Halden, Norway

    Sanjay Misra

  3. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Amos O. Bajeh

  4. Department of Geography, University of Ibadan, Ibadan, Nigeria

    Ufuoma Odomero Okoro

  5. Vishwakarma Skill University, Gurgaon, India

    Ravin Ahuja

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  1. Roseline Oluwaseun Ogundokun
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  2. Sanjay Misra
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  3. Amos O. Bajeh
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  4. Ufuoma Odomero Okoro
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  5. Ravin Ahuja
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Correspondence to Roseline Oluwaseun Ogundokun .

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Editors and Affiliations

  1. Østfold University College, Halden, Norway

    Sanjay Misra

  2. Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

    Chamundeswari Arumugam

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Ogundokun, R.O., Misra, S., Bajeh, A.O., Okoro, U.O., Ahuja, R. (2022). An Integrated IDS Using ICA-Based Feature Selection and SVM Classification Method. In: Misra, S., Arumugam, C. (eds) Illumination of Artificial Intelligence in Cybersecurity and Forensics. Lecture Notes on Data Engineering and Communications Technologies, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-030-93453-8_11

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