Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems

被引:10
|
作者
Almuqren, Latifah [1 ]
Maashi, Mashael S. [2 ]
Alamgeer, Mohammad [3 ]
Mohsen, Heba [4 ]
Hamza, Manar Ahmed [5 ]
Abdelmageed, Amgad Atta [5 ]
机构
[1] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[2] King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, POB 103786, Riyadh 11543, Saudi Arabia
[3] King Khalid Univ, Coll Sci & Art Mahayil, Dept Informat Syst, Abha 62529, Saudi Arabia
[4] Future Univ Egypt, Fac Comp & Informat Technol, Dept Comp Sci, New Cairo 11835, Egypt
[5] Prince Sattam bin Abdulaziz Univ, Dept Comp & Self Dev, Al Kharj 16278, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 05期
关键词
security; intrusion detection; cyber-physical systems; explainable artificial intelligence; feature selection;
D O I
10.3390/app13053081
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Artificial intelligence for securing industrial-based cyber-physical systems
    Lv, Zhihan
    Chen, Dongliang
    Lou, Ranran
    Alazab, Ammar
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2021, 117 : 291 - 298
  • [32] A Cyber-Physical Power System Test Bed for Intrusion Detection Systems
    Adhikari, Uttam
    Morris, Thomas H.
    Pan, Shengyi
    2014 IEEE PES GENERAL MEETING - CONFERENCE & EXPOSITION, 2014,
  • [33] Towards Sustainable Models of Computation for Artificial Intelligence in Cyber-Physical Systems
    Pirani, Massimiliano
    Dragoni, Aldo Franco
    Longhi, Sauro
    IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2021,
  • [34] Explainable Artificial Intelligence into Cyber-Physical System Architecture of Smart Cities: Technologies, Challenges, and Opportunities
    Batra, Isha
    Malik, Arun
    Sharma, Shamneesh
    Sharma, Chetan
    Hosen, A. S. M. Sanwar
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (02) : 2343 - 2362
  • [35] Interpretable Detection of Distribution Shifts in Learning Enabled Cyber-Physical Systems
    Yang, Yahan
    Kaur, Ramneet
    Dutta, Souradeep
    Lee, Insup
    2022 13TH ACM/IEEE INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2022), 2022, : 225 - 235
  • [36] Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems
    Gutierrez-Rojas, Daniel
    Kalalas, Charalampos
    Christou, Ioannis
    Almeida, Gustavo
    Eldeeb, Eslam
    Bakri, Sihem
    Marchetti, Nicola
    Sant'Ana, Jean M. S.
    Lopez, Onel L. Alcaraz
    Alves, Hirley
    Papadias, Constantinos
    Haroon Tariq, Muhammad
    Nardelli, Pedro H. J.
    IEEE SENSORS JOURNAL, 2025, 25 (04) : 7193 - 7204
  • [37] Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical Systems
    Mahmud, Syeda Aunanya
    Islam, Nazmul
    Islam, Zahidul
    Rahman, Ziaur
    Mehedi, Sk. Tanzir
    MATHEMATICS, 2024, 12 (20)
  • [38] Explainable AI for Cyber-Physical Systems: Issues and Challenges
    Hoenig, Amber
    Roy, Kaushik
    Acquaah, Yaa Takyiwaa
    Yi, Sun
    Desai, Salil S.
    IEEE ACCESS, 2024, 12 : 73113 - 73140
  • [39] Secure estimation and attack detection in cyber-physical systems with switching attack
    Martynova, Dina
    Zhang, Ping
    2018 EUROPEAN CONTROL CONFERENCE (ECC), 2018, : 357 - 362
  • [40] Towards Self-Explainable Cyber-Physical Systems
    Blumreiter, Mathias
    Greenyer, Joel
    Garcia, Francisco Javier Chiyah
    Kloes, Verena
    Schwammberger, Maike
    Sommer, Christoph
    Vogelsang, Andreas
    Wortmann, Andreas
    2019 ACM/IEEE 22ND INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS COMPANION (MODELS-C 2019), 2019, : 543 - 548