4.7 Article

Cross-Layer Defense Methods for Jamming-Resistant CBTC Systems

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3005931

Keywords

Jamming; Games; Security; Control systems; Physical layer; Stochastic processes; Communication systems; Communication-based train control (CBTC); security; cross-layer defense; stochastic game

Funding

  1. Fundamental Research Funds for the Central Universities [2018JBM076]
  2. Natural Science Foundation of China [61973026]
  3. Beijing Municipal Education Commission [I20H100010, I19H100010]
  4. Beijing Science and Technology Commission [Z191100010818001]
  5. Beijing Natural Science Foundation [L181004, RCS2019ZT010, RCS2018K008, 2018JBZ002]
  6. Beijing Traffic Control Technology (BJTCT) [9907006509]

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Communication-based Train Control (CBTC) systems are the future direction for train control systems, but they are more vulnerable to cyber-attacks. This paper proposes a cross-layer defense scheme including a model prediction control algorithm, a multi-stage stochastic game model, and a dynamic train travel speed profile generation algorithm to mitigate the impact of jamming attacks on CBTC systems. The simulation results show that the proposed defense scheme effectively mitigates the impact of jamming attacks.
Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks' impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme.

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