An Explainable and Resilient Intrusion Detection System for Industry 5.0

Danish Javeed, Tianhan Gao, Prabhat Kumar, Alireza Jolfaei

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Industry 5.0 is a emerging transformative model that aims to develop a hyperconnected, automated, and data-driven industrial ecosystem. This digital transformation will boost productivity and efficiency throughout the production process but will be more prone to new sophisticated cyber-attacks. Deep learning-based Intrusion Detection Systems (IDS) have the potential to recognize intrusions with high accuracy. However, these models are complex and are treated as a black box by developers and security analysts due to the inability to interpret the decisions made by these models. Motivated by the challenges, this paper presents an explainable and resilient IDS for Industry 5.0. The proposed IDS is designed by combining bidirectional long short-term memory networks (BiLSTM), a bidirectional-gated recurrent unit (Bi-GRU), fully connected layers and a softmax classifier to enhance the intrusion detection process in Industry 5.0. We employ the SHapley Additive exPlanations (SHAP) mechanism to interpret and understand the features that contributed the most in the decision of the proposed cyber-resilient IDS. The evaluation of the proposed model using the explainability can ensure that the model is working as expected. The experimental results based on the CICDDoS2019 dataset confirms the superiority of the proposed IDS over some recent approaches.

Original languageEnglish
Pages (from-to)1342-1350
Number of pages9
JournalIEEE Transactions on Consumer Electronics
Volume70
Issue number1
Early online date7 Jun 2023
DOIs
Publication statusPublished - 1 Feb 2024

Keywords

  • Computer architecture
  • Cyber-Attacks
  • Cyberattack
  • Data models
  • Deep Learning (DL)
  • Explainable Artificial Intelligence
  • Industries
  • Industry 5.0
  • Intrusion detection
  • Intrusion Detection System (IDS)
  • Logic gates
  • Security
  • explainable artificial intelligence
  • cyber-attacks
  • Deep learning (DL)
  • intrusion detection system (IDS)

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