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An Efficient Hybrid Approach Using Misuse Detection and Genetic Algorithm for Network Intrusion Detection

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Advances in Computing and Data Sciences (ICACDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 905))

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Abstract

In today’s fast-changing Information Technology world, even the best available security is deficient for the latest vulnerabilities. In order to protect data and system integrity, Intrusion Detection is a preferred choice of researchers. In this paper, we have proposed a hybrid approach for intrusion detection that is based on misuse detection and genetic algorithm approach. Here, feature selection technique has been used for extracting important features and genetic algorithm is used for generating new rules. In this paper, we have detected ten different types of attacks that have high detection as well as low false positive rates.

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Authors and Affiliations

  1. CSED, Thapar Institute of Engineering and Technology, Patiala, 147004, India

    Rohini Rajpal & Sanmeet Kaur

Authors
  1. Rohini Rajpal
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  2. Sanmeet Kaur
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Corresponding author

Correspondence to Sanmeet Kaur .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague 8, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, Canada

    Tuncer Ören

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Rajpal, R., Kaur, S. (2018). An Efficient Hybrid Approach Using Misuse Detection and Genetic Algorithm for Network Intrusion Detection. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_22

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  • DOI: https://doi.org/10.1007/978-981-13-1810-8_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1809-2

  • Online ISBN: 978-981-13-1810-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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