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Benchmarking Behavior-Based Intrusion Detection Systems with Bio-inspired Algorithms

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Security in Computing and Communications (SSCC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1364))

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Abstract

Network security encompasses distinct technologies and protocols, being behaviour based network Intrusion Detection Systems (IDS) a promising application to detect and identify zero-day attacks and vulnerabilities exploits. In order to overcome the weaknesses of signature-based IDS, behaviour-based IDS applies a wide set of machine learning technologies to learn the normal behaviour of the network, making it possible to detect malicious and not yet seen activities. The machine learning techniques that can be applied to IDS are vast, as are the methods to generate the datasets used for testing. This paper aims to evaluate CSE-CIC-IDS2018 dataset and benchmark a set of supervised bioinspired machine learning algorithms, namely CLONALG Artificial Immune System, Learning Vector Quantization (LVQ) and Back-Propagation Multi-Layer Perceptron (MLP). The results obtained were also compared with an ensemble strategy based on a majority voting algorithm. The results obtained show the appropriateness of using the dataset to test behaviour based network intrusion detection algorithms and the efficiency of MLP algorithm to detect zero-day attacks, when comparing with CLONALG and LVQ.

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Author information

Authors and Affiliations

  1. School of Technology and Management, Polytechnic of Leiria, Leiria, Portugal

    Paulo Ferreira & Mário Antunes

  2. CIIC, School of Technology and Management, Polytechnic of Leiria - Portugal, Leiria, Portugal

    Mário Antunes

  3. INESC-TEC, CRACS, University of Porto - Portugal, Porto, Portugal

    Mário Antunes

Authors
  1. Paulo Ferreira
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  2. Mário Antunes
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Correspondence to Mário Antunes .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management - Kerala, Trivandrum, India

    Sabu M. Thampi

  2. Guangzhou University, Guangzhou, China

    Guojun Wang

  3. Howard University, Washington, DC, USA

    Danda B. Rawat

  4. University of Queensland, Brisbane, QLD, Australia

    Ryan Ko

  5. National Sun Yat-sen University, Kaohsiung, Taiwan

    Chun-I Fan

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Ferreira, P., Antunes, M. (2021). Benchmarking Behavior-Based Intrusion Detection Systems with Bio-inspired Algorithms. In: Thampi, S.M., Wang, G., Rawat, D.B., Ko, R., Fan, CI. (eds) Security in Computing and Communications. SSCC 2020. Communications in Computer and Information Science, vol 1364. Springer, Singapore. https://doi.org/10.1007/978-981-16-0422-5_11

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  • DOI: https://doi.org/10.1007/978-981-16-0422-5_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0421-8

  • Online ISBN: 978-981-16-0422-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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