Generative AI and Cognitive Computing-Driven Intrusion Detection System in Industrial CPS

Author:

Islam Shareeful,Javeed Danish,Saeed Muhammad Shahid,Kumar Prabhat,Jolfaei Alireza,Islam A. K. M. Najmul

Abstract

AbstractIndustrial Cyber-Physical Systems (ICPSs) are becoming more and more networked and essential to modern infrastructure. This has led to an increase in the complexity of their dynamics and the challenges of protecting them from advanced cyber threats have escalated. Conventional intrusion detection systems (IDS) often struggle to interpret high-dimensional, sequential data efficiently and extract meaningful features. They are characterized by low accuracy and a high rate of false positives. In this article, we adopt the computational design science approach to design an IDS for ICPS, driven by Generative AI and cognitive computing. Initially, we designed a Long Short-Term Memory-based Sparse Variational Autoencoder (LSTM-SVAE) technique to extract relevant features from complex data patterns efficiently. Following this, a Bidirectional Recurrent Neural Network with Hierarchical Attention (BiRNN-HAID) is constructed. This stage focuses on proficiently identifying potential intrusions by processing data with enhanced focus and memory capabilities. Next, a Cognitive Enhancement for Contextual Intrusion Awareness (CE-CIA) is designed to refine the initial predictions by applying cognitive principles. This enhances the system’s reliability by effectively balancing sensitivity and specificity, thereby reducing false positives. The final stage, Interpretive Assurance through Activation Insights in Detection Models (IAA-IDM), involves the visualizations of mean activations of LSTM and GRU layers for providing in-depth insights into the decision-making process for cybersecurity analysts. Our framework undergoes rigorous testing on two publicly accessible industrial datasets, ToN-IoT and Edge-IIoTset, demonstrating its superiority over both baseline methods and recent state-of-the-art approaches.

Funder

LUT University (previously Lappeenranta University of Technology

Publisher

Springer Science and Business Media LLC

Reference37 articles.

1. Yu X, Xue Y. Smart grids: a cyber-physical systems perspective. Proc IEEE. 2016;104(5):1058–70.

2. Kayan H, Nunes M, Rana OF, Burnap P, Perera C. Cybersecurity of Industrial Cyber-physical Systems: a review. ACM Comput Surv (CSUR). 2021;54:1–35.

3. Wright JG, Wolthusen SD. Access control and availability vulnerabilities in the iso/iec 61850 substation automation protocol. In Grigore Havarneanu, Roberto Setola, Hypatia Nassopoulos, and Stephen Wolthusen, editors, Critical Information Infrastructures Security, pages 239–251. Springer International Publishing. 2017.

4. Tidy J. How a ransomware attack cost one firm £45m. BBC; 2019. https://www.bbc.com/news/business-48661152. Accessed 10 Mar 1999.

5. Radiflow Team. Radiflow reveals first documented cryptocurrency malware attack on a SCADA network. radiflow; 2018. https://www.radiflow.com/news/radiflow-reveals-first-documented-cryptocurrency-malware-attack-on-a-scada-network/. Accessed 15 Mar 2023.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3