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International Conference on the Applications of Evolutionary Computation (Part of EvoStar)

EvoApplications 2019: Applications of Evolutionary Computation pp 395–404Cite as

Early Detection of Botnet Activities Using Grammatical Evolution

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11454))

Abstract

There have been numerous studies proposed for detecting botnets in the literature. However, it is still a challenging issue as most of the proposed systems are unable to detect botnets in their early stage and they cannot perform satisfying performance on new forms of botnets. In this study, we propose an evolutionary computation-based approach that relies on grammatical evolution to generate a botnet detection algorithm automatically. The performance of the proposed flow-based detection system reveals that it detects botnets accurately in their very early stage and performs better than most of the existing methods.

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Author information

Authors and Affiliations

  1. WISE Lab., Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Selim Yilmaz & Sevil Sen

Authors
  1. Selim Yilmaz
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  2. Sevil Sen
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Corresponding author

Correspondence to Selim Yilmaz .

Editor information

Editors and Affiliations

  1. University of Mainz, Mainz, Germany

    Paul Kaufmann

  2. University of Granada, Granada, Spain

    Pedro A. Castillo

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Yilmaz, S., Sen, S. (2019). Early Detection of Botnet Activities Using Grammatical Evolution. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_26

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  • DOI: https://doi.org/10.1007/978-3-030-16692-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16691-5

  • Online ISBN: 978-3-030-16692-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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