Designing energy-aware collaborative intrusion detection in IoT networks

Wenjuan LI, Philip ROSENBERG, Mads GLISBY, Michael HAN

Research output: Contribution to journalArticlespeer-review

Abstract

The Internet of Things (IoT) with its evolution brings many benefits to people's routine life, while at the same time posing various security challenges, due to the lack of software updates and access control policies. For protection, collaborative/distributed detection networks are one essential security solution, which can enhance the detection capability of a separate detection node by governing a set of eligible detection nodes to share information. However, internal intruders/attacks are the main threat source to the distributed structure including both IoT and CIDNs. In the literature, the CIDNs with challenge-based trust mechanism is found to be secure under many common external threats, but would be ineffective to advanced insider threats. Motivated by this issue, this work considers the factor of energy consumption and develops an enhanced challenge-based CIDN, called EnergyCIDN. The main idea relies on the observation that advanced attacks should consume more energy than a benign node. Under the evaluation on our EnergyCIDN, we conduct a set of experiments and tests in many scenarios, including simulated network, practical IoT network, and medical network. In comparison with the existing similar approaches, our EnergyCIDN showcases better detection capability regarding both common and advanced insider attacks. Copyright © 2024 Published by Elsevier Ltd.

Original languageEnglish
Article number103713
JournalJournal of Information Security and Applications
Volume81
Early online dateJan 2024
DOIs
Publication statusPublished - Mar 2024

Citation

Li, W., Rosenberg, P., Glisby, M., & Han, M. (2024). Designing energy-aware collaborative intrusion detection in IoT networks. Journal of Information Security and Applications, 81, Article 103713. https://doi.org/10.1016/j.jisa.2024.103713

Keywords

  • Collaborative detection
  • Intrusion detection
  • Trust management
  • Energy consumption
  • Insider threat

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