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Improving security using SVM-based anomaly detection: issues and challenges

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Abstract

Security is one of the main requirements of the current computer systems, and recently it gains much importance as the number and severity of malicious attacks increase dramatically. Anomaly detection is one of the main branches of the intrusion detection systems which enables to recognize the newer variants of the security attacks. This paper focuses on the anomaly detection schemes (ADS), which have applied support vector machine (SVM) for detecting intrusions and security attacks. For this purpose, it first presents the required concepts about the SVM classifier and intrusion detection systems. It then classifies the ADS approaches and discusses the various machine learning and artificial intelligence techniques that have been applied in combination with the SVM classifier to detect anomalies. Besides, it specifies the primary capabilities, possible limitations, or advantages of the ADS approaches. Furthermore, a comparison of the studied ADS schemes is provided to illuminate their various technical details.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mehdi Hosseinzadeh

  2. Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh

  3. Department of Computer Science, Khazar University, Baku, Azerbaijan

    Amir Masoud Rahmani

  4. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Bay Vo

  5. Computer Engineering Department, Urmia Branch, Islamic Azad University, Urmia, Iran

    Moazam Bidaki

  6. Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

    Mohammad Masdari

  7. Afagh Higher Education Institute, Urmia, Iran

    Mehran Zangakani

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  1. Mehdi Hosseinzadeh
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Hosseinzadeh, M., Rahmani, A.M., Vo, B. et al. Improving security using SVM-based anomaly detection: issues and challenges. Soft Comput 25, 3195–3223 (2021). https://doi.org/10.1007/s00500-020-05373-x

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