Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system

Zhoukai Wang; Xinhong Hei; Weigang Ma; Yichuan Wang; Kan Wang; Qiao Jia; Zhoukai Wang; Xinhong Hei; Weigang Ma; Yichuan Wang; Kan Wang; Qiao Jia

doi:10.3934/mbe.2022015

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 1: 287-308. doi: 10.3934/mbe.2022015

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Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system

1.
College of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China
2.
Shaanxi Provincial Key Laboratory of Network Computing and Security Technology, Xi'an 710048, China

Academic Editor: Weizheng Wang

Received: 23 September 2021 Accepted: 06 November 2021 Published: 12 November 2021

With the rapid development of the high-speed train industry, the high-speed train control system has now been exposed to a complicated network environment full of dangers. This paper provides a speculative parallel data detection algorithm to rapidly detect the potential threats and ensure data transmission security in the railway network. At first, the structure of the high-speed train control data received by the railway control center was analyzed and divided tentatively into small chunks to eliminate the inside dependencies. Then the traditional threat detection algorithm based on deterministic finite automaton was reformed by the speculative parallel optimization so that the inline relationship's influences that affected the data detection order could be avoided. At last, the speculative parallel detection algorithm would inspect the divided data chunks on a distributed platform. With the help of both the speculative parallel technique and the distributed platform, the detection deficiency for train control data was improved significantly. The results showed that the proposed algorithm exhibited better performance and scalability when compared with the traditional, non-parallel detection method, and massive train control data could be inspected and processed promptly. Now it has been proved by practical use that the proposed algorithm was stable and reliable. Our local train control center was able to quickly detect the anomaly and make a fast response during the train control data transmission by adopting the proposed algorithm.
- cybersecurity,
- high-speed train,
- control system,
- parallel computing,
- distributed computing
Citation: Zhoukai Wang, Xinhong Hei, Weigang Ma, Yichuan Wang, Kan Wang, Qiao Jia. Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 287-308. doi: 10.3934/mbe.2022015

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Abstract

With the rapid development of the high-speed train industry, the high-speed train control system has now been exposed to a complicated network environment full of dangers. This paper provides a speculative parallel data detection algorithm to rapidly detect the potential threats and ensure data transmission security in the railway network. At first, the structure of the high-speed train control data received by the railway control center was analyzed and divided tentatively into small chunks to eliminate the inside dependencies. Then the traditional threat detection algorithm based on deterministic finite automaton was reformed by the speculative parallel optimization so that the inline relationship's influences that affected the data detection order could be avoided. At last, the speculative parallel detection algorithm would inspect the divided data chunks on a distributed platform. With the help of both the speculative parallel technique and the distributed platform, the detection deficiency for train control data was improved significantly. The results showed that the proposed algorithm exhibited better performance and scalability when compared with the traditional, non-parallel detection method, and massive train control data could be inspected and processed promptly. Now it has been proved by practical use that the proposed algorithm was stable and reliable. Our local train control center was able to quickly detect the anomaly and make a fast response during the train control data transmission by adopting the proposed algorithm.

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