File(s) under permanent embargo
Reason: This item is currently closed access.
Design and analysis of multimodel-based anomaly intrusion detection systems in industrial process automation
journal contributionposted on 26.11.2015, 14:24 by Chunjie Zhou, Shuang Huang, Naixue Xiong, Shuang-Hua Yang, Huiyun Li, Yuanqing Qin, Xuan Li
Industrial process automation is undergoing an increased use of information communication technologies due to high flexibility interoperability and easy administration. But it also induces new security risks to existing and future systems. Intrusion detection is a key technology for security protection. However, traditional intrusion detection systems for the IT domain are not entirely suitable for industrial process automation. In this paper, multiple models are constructed by comprehensively analyzing the multidomain knowledge of field control layers in industrial process automation, with consideration of two aspects: physics and information. And then, a novel multimodel-based anomaly intrusion detection system with embedded intelligence and resilient coordination for the field control system in industrial process automation is designed. In the system, an anomaly detection based on multimodel is proposed, and the corresponding intelligent detection algorithms are designed. Furthermore, to overcome the disadvantages of anomaly detection, a classifier based on an intelligent hidden Markov model, is designed to differentiate the actual attacks from faults. Finally, based on a combination simulation platform using optimized performance network engineering tool, the detection accuracy and the real-Time performance of the proposed intrusion detection system are analyzed in detail. Experimental results clearly demonstrate that the proposed system has good performance in terms of high precision and good real-Time capability.
The work is supported by the National Natural Science Foundation of China (No.61272204) and the Fundamental Research Funds for the Central Universities of China (HUST: No. 2013ZZGH006).
- Computer Science