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. In real life, for example, in network systems, a network node (router or user) may be attacked or unable to correctly and promptly transmit messages due to other reasons; in power systems, a current or voltage detector may naturally age or be affected by external factors, thus failing to accurately obtain the current and voltage values of the power system; in automotive systems, damage to a component may cause the sensor readings to differ from the original values, and so on. We cannot guarantee that all components in a system will always function properly and remain free from external attacks. CA-diagnosability refers to the system's ability to determine the occurrence of fault events when sensor readings are modified. This is key to ensuring the normal operation of the system.
In order to efficiently determine the CA - diagnosability of DESs, a research team led by Dantong Ouyang published their new research on 15 June 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed a novel cyclic model (CM), which increases the efficiency of checking the system's CA-diagnosability without constructing the diagnoser.
They first initiate an innovative algorithm, the detection of cycles (DC), to get cyclic information for constructing the CM. Subsequently, they 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, they delineate the sufficient and necessary conditions for CA-dia-gnosability within the CM framework and offer an analysis of its algorithmic complexity. They 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.Tables 1-3 respectively present the experimental results of checking CA-diagnosability under three types of static attacks.
DOI:10.1007/s11704-025-41289-1