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How Good Are Classification Models in Handling Dynamic Intrusion Attacks in IoT?

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Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 185))

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Abstract

Internet of things (IoT) is vulnerable to the intrusion that may lead to security threats in the IoT ecosystem. Due to different architecture and protocol stack, the traditional intrusion detection system (IDS) does not work well for generating alarm during possible intrusion in IoT. Machine learning is one of the potential tools for effective intrusion detection. However, to apply them in IoT, it may need customization to work with IoT traffic. The situation becomes adverse when the attack patterns are not known Apriori. To mislead IDS, attackers frequently change the attack patterns. As a result, traditional machine learning methods usually fail to handle such dynamic intrusion effectively. In this work, we try to assess seven (07) well-known classification models for their suitability in the IoT network in detecting novel/dynamic attacks. It is more vulnerable and lethal for a system, if a detection system misclassifies a novel (unseen) attack as normal traffic. During our study, we assess such scenario of misclassification by our candidate models. Our result reveals that random forest performs better in detecting seen IoT attacks. SVM is superior in keeping a low misclassification rate for dynamic attacks as regular traffic. Our investigation further concludes that the best IDS system is not always the best detector for handling novel attacks.

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Notes

  1. 1.

    The words novel/unseen/unknown/dynamic for never seen before the attack will be used interchangeably.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/.

  3. 3.

    https://www.cs.waikato.ac.nz/ml/weka/.

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Authors and Affiliations

  1. Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, Sikkim, India

    Lekhika Chettri & Swarup Roy

Authors
  1. Lekhika Chettri
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  2. Swarup Roy
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Correspondence to Swarup Roy .

Editor information

Editors and Affiliations

  1. School of computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Siba K. Udgata

  2. IGIT Sarang, Dhenkanal, Odisha, India

    Srinivas Sethi

  3. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Satish N. Srirama

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Chettri, L., Roy, S. (2021). How Good Are Classification Models in Handling Dynamic Intrusion Attacks in IoT?. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_8

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