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Cyber Physical System for Distributed Network Using DoS Based Hierarchical Bayesian Network
DOI:10.1007/s10723-023-09662-1.png)
Abstract
En 中文
The Cyber Physical System (CPS) is a prime target for cyber attacks due to its heterogeneity and connectivity with physical equipment. This paper proposes a model based on a Hierarchical Bayesian Network (HBN) to increase the CPS's attack detection ability. Denial of Service (DoS) attacks pose a significant threat to production line availability, business services, and human lives. Therefore, this paper focuses on detecting DoS attacks using the Bayesian network model, an efficient algorithm to detect faults in the networking system based on incomplete recognizing information. The standard Bayesian network model is hierarchically enhanced and optimized with a Bacterial Foraging Optimization (BFO) approach to improve the detection process. The developed, optimized model satisfies security, Quality of Service (QoS) requirements, and time consumption by reducing the constraints to obtain system reliability. The efficiency of the proposed model is evaluated using the NSL-KDD dataset and compared with existing approaches in terms of accuracy, precision, recall, F1-score, ROC, and RMSE. Compared to other existing systems, the proposed model achieves an accuracy of 98.4% in detecting DoS attacks with a reduced RMSE of 0.0617.
Keywords:
Cyber-physical system (CPS)
Bayesian network
Optimization
Bacterial foraging
distributed network
Denial of Service
Bird swarm optimization
Journal
IF:
2.9
Papers:
759
Citations:
1.2K

