A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems

A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems

Blog Article

Water Distribution System Diary (WDS) threats have significantly grown following the Maroochy shire incident, as evidenced by proofed attacks on water premises.As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases.The attack detection system must meet two critical requirements: high accuracy and near real-time detection.

This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks.Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss.Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Caffeinated Coffee Forest.Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators.Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability.

In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy.As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.