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<p>The cyber-attack diagnosability (CA-diagnosability) of discrete event systems (DESs) assess the ability to diagnose issues when an attacker interferes with sensor-to-diagnostic communication. This paper introduces a novel cyclic model (CM), which increases the efficiency of checking the system’s CA-diagnosability without constructing the diagnoser. We first initiate an innovative algorithm, the detection of cycles (DC), to get cyclic information for constructing the CM. Subsequently, we expand upon the concept of critical observations to diagnosability checking and propose the getting critical observations (GCO) algorithm. Finally, in the proposal of the CM-based CA-diagnos-ability checking (CMDIC) algorithm, we delineate the sufficient and necessary conditions for CA-diagnosability within the CM framework and offer an analysis of its algorithmic complexity. We demonstrates findings with an example of faults in a power system’s protection relay and circuit breaker. Experimental results on different benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods in multi-fault systems, with an average improvement of over 95%. In the best-case scenarios, the improvement can reach up to two orders of magnitude.</p>

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A novel efficient model for testing diagnosability of discrete event systems under sensor attacks

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Qifei Li,
  • Dantong Ouyang,
  • Xiangfu Zhao,
  • Luyu Jiang,
  • Ran Tai,
  • Liming Zhang

摘要

The cyber-attack diagnosability (CA-diagnosability) of discrete event systems (DESs) assess the ability to diagnose issues when an attacker interferes with sensor-to-diagnostic communication. This paper introduces a novel cyclic model (CM), which increases the efficiency of checking the system’s CA-diagnosability without constructing the diagnoser. We first initiate an innovative algorithm, the detection of cycles (DC), to get cyclic information for constructing the CM. Subsequently, we expand upon the concept of critical observations to diagnosability checking and propose the getting critical observations (GCO) algorithm. Finally, in the proposal of the CM-based CA-diagnos-ability checking (CMDIC) algorithm, we delineate the sufficient and necessary conditions for CA-diagnosability within the CM framework and offer an analysis of its algorithmic complexity. We demonstrates findings with an example of faults in a power system’s protection relay and circuit breaker. Experimental results on different benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods in multi-fault systems, with an average improvement of over 95%. In the best-case scenarios, the improvement can reach up to two orders of magnitude.