EnergyCIDN: Enhanced energy-aware challenge-based collaborative intrusion detection in internet of things

Wenjuan LI, Philip ROSENBERG, Mads GLISBY, Michael HAN

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

Abstract

With cyber attacks becoming more complex and advanced, a separate intrusion detection system (IDS) is believed to be insufficient for protecting the whole computer networks. Thus, collaborative intrusion detection networks (CIDNs) are proposed aiming to improve the detection performance by allowing various nodes to share required information or messages with other nodes. To defeat insider threats during the sharing process (e.g., malicious information), trust management is a necessary security mechanism for CIDNs, where challenge-based CIDNs are a typical example that sends a special kind of message, called challenge, to evaluate the reputation of a node. The previous work has proven that challenge-based CIDNs can defeat most common insider threats, but it may still suffer from some advanced insider threats, e.g., passive message fingerprint attack (PMFA). In this work, we develop EnergyCIDN, an enhanced challenge-based CIDN by adopting an energy-aware trust management model against advanced insider attacks. In the evaluation, we study the performance of EnergyCIDN under both simulated and practical Internet of Things (IoT) environments. The results demonstrate that EnergyCIDN can perform better than many similar schemes in identifying advanced malicious nodes. Copyright © 2023 Springer Nature Switzerland AG.

Original languageEnglish
Title of host publicationAlgorithms and architectures for parallel processing: 22nd International Conference, ICA3PP 2022, Copenhagen, Denmark, October 10–12, 2022, proceedings
EditorsWeizhi MENG, Rongxing LU, Geyong MIN, Jaideep VAIDYA
Place of PublicationCham
PublisherSpringer
Pages293-312
ISBN (Electronic)9783031226779
ISBN (Print)9783031226762
DOIs
Publication statusPublished - 2023

Citation

Li, W., Rosenberg, P., Glisby, M., & Han, M. (2023). EnergyCIDN: Enhanced energy-aware challenge-based collaborative intrusion detection in internet of things. In W. Meng, R. Lu, G. Min, & J. Vaidya (Eds.), Algorithms and architectures for parallel processing: 22nd International Conference, ICA3PP 2022, Copenhagen, Denmark, October 10–12, 2022, proceedings (pp. 293-312). Springer. https://doi.org/10.1007/978-3-031-22677-9_16

Keywords

  • Intrusion detection
  • Collaborative network
  • Insider attack
  • Energy consumption
  • Trust management

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