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Efficient Techniques for Attack Detection Using Different Features Selection Algorithms and Classifiers

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Abstract

With the growth and benefits of network usage, securing the networks by using anomaly intrusion detection systems (IDS) against unknown intrusions has become an important issue. The first step of protecting any network is the detection of attacks. In this paper, we concentrate on four attacks; denial of service (DoS), probing, remote-to-local, and user-to-root attacks. We depend on features extracted from (NSL-KDD) dataset for these attacks. We investigate the performance of the attack detection process for several numbers of features using various subset-based feature selection techniques aiming to find the optimum collection of features for detecting each attack with an appropriate classifier. Simulation results reveal that redundant features can be eliminated from the attack detection process, and that we can determine the most useful set of features for a certain classifier, which enhances the IDS performance.

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Authors and Affiliations

  1. El-Sadat University, Sadat, Egypt

    Rania A. Ghazy

  2. Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    El-Sayed M. EL-Rabaie, Moawad I. Dessouky & Fathi E. Abd El-Samie

  3. Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Nawal A. El-Fishawy

Authors
  1. Rania A. Ghazy
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  2. El-Sayed M. EL-Rabaie
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  3. Moawad I. Dessouky
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  4. Nawal A. El-Fishawy
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  5. Fathi E. Abd El-Samie
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Correspondence to Fathi E. Abd El-Samie.

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Ghazy, R.A., EL-Rabaie, ES.M., Dessouky, M.I. et al. Efficient Techniques for Attack Detection Using Different Features Selection Algorithms and Classifiers. Wireless Pers Commun 100, 1689–1706 (2018). https://doi.org/10.1007/s11277-018-5662-0

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  • DOI: https://doi.org/10.1007/s11277-018-5662-0

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