A weighted graph‐based method for detection of data integrity attacks in electricity markets

Abstract

Abstract The negative impacts of data integrity attacks against multi‐settlement electricity markets have been recently studied. It has been shown that adversaries could launch profitable cyber attacks by casting an incorrect image of transmission lines' congestion pattern while simultaneously submitting bogus bids in electricity markets. Although a variety of countermeasures have been proposed in the literature to detect such data integrity attacks, none of those approaches has been designed specifically to ensure data integrity of multi‐settlement electricity markets. The existing methods may even fail to detect complicated types of attacks (e.g. unobservable false data injection attacks) or fail to address practical constraints and requirements of the electricity markets. As an example, most of the attack detection approaches have been established based on the DC economic dispatch, while the modern electricity markets may use AC models. This paper proposes a new anomaly detection tool based on graph theory using so‐called egonets, specifically designed to detect false data injection attacks against electricity markets. In contrast with the conventional detection methods, the malicious intention of the adversaries is exploited in the design procedure. Therefore, the proposed approach can detect false data injection attacks, regardless of the attack type and power flow model.

Keywords

• Data security
• Combinatorial mathematics
• Combinatorial mathematics
• Power system management, operation and economics
• Power transmission, distribution and supply
• Power system protection