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
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