EX-DFL : an explainable deep federated-based intrusion detection system for industrial IoT

Name: Attique, Danish

Organization : Chongqing University of Posts and Telecommunications. College of Computer Science and Technology

Email : L202010008@stu.cqupt.edu.cn

Name: Hao, Wang

Organization : Chongqing University of Posts and Telecommunications. College of Computer Science and Technology

Email : wanghao@cqupt.edu.cn

Name: Ping, Wang

Organization : Chongqing University of Posts and Telecommunications. Department of Automation

Email : wangping@cqupt.edu.cn

Name: Javeed, Danish

Organization : Northeastern University. Software College

Email : 2027016@stu.neu.edu.cn

Name: Adil, Muhammad

Organization : Tianjin University. College of Intelligence and Computing

Email : Muhammadadil294@yahoo.com

keyword: Explainable artificial intelligence

LCSH: Intrusion detection systems (Computer security)

LCSH: Federated learning (Machine learning)

LCSH: Artificial intelligence

LCSH: Deep learning (Machine learning)

Abstract: With the escalating volume of sensitive data transmitted in Industrial Internet of Things (IIoT) infrastructures, securing data produced by various IIoT devices has become paramount for safeguarding critical industrial processes. In particular, the field of Industrial IoT (IIoT) is witnessing an increasing threat landscape, necessitating robust Intrusion Detection Systems (IDS) to establish a secure environment. This paper addresses the security concerns associated with IIoT by proposing a Federated Learning (FL)-based IDS. The focus is on preserving data privacy, a critical consideration in industrial settings. The proposed explainable approach employs a Deep Neural Network in FL (EX-DFL) to detect anomalies in IIoT traffic, mitigating potential security threats. A key contribution of this work is the incorporation of Explainable AI (XAI) techniques, specifically leveraging the SHapley Additive exPlanations (SHAP) library, to enhance the interpretability of the IDS decisions. This addresses the challenge of understanding the rationale behind the model’s predictions in complex industrial environments. The proposed EX-DFL performance is assessed using various standard metrics and evaluated on the CICIDS2017 dataset, demonstrating promising results by achieving over 99% accuracy in anomaly detection.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2024

Modified: 2025-05-27

Issued: 2025-05-27

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BibliograpyCitation : In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.358-364). Phuket : Prince of Songkla University

eng

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