Bad Data Injection Attack and Defense in Electricity Market using Game Theory Study (1210.3252v1)

Abstract: Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5 Bus test system, we can show the effectiveness and properties of the studied game.

• The paper investigates bad data injection (BDI) attacks in electricity markets and models defense strategies using a zero-sum game theory approach.
• Through simulations on a power system model, the study demonstrates the game theory model's efficacy in predicting outcomes and suggests optimal strategies for attacking and defending measurements.
• The research highlights the economic impact of BDI attacks on market prices and discusses future directions including advanced attack models, machine learning defenses, and scaling to larger grid systems.

Insights into Bad Data Injection Attack and Defense in Electricity Market via Game Theory

The integration of advanced cyber technologies into the monitoring and control systems of smart grids has exposed them to various security vulnerabilities, notably bad data injection (BDI) attacks. This paper presents a comprehensive investigation into the implications of BDI attacks on the electricity market and proposes a defense mechanism modeled as a zero-sum game between an attacker and a defender.

Smart grids enhance energy management through bi-directional flows of electricity and information, which are crucial for state estimation (SE) in energy management centers. However, the paper elucidates how malicious entities can exploit this setup by injecting false data into essential measurements, thereby manipulating price settings within electricity markets. The impact of such cyber-attacks is significant, potentially causing escalations or reductions in electricity prices, negatively impacting grid stability, and producing economic disturbances.

The paper explores how attackers can observe market transactions and modify estimated power transmissions strategically to alter congestion levels, thereby skewing market prices to gain financial benefits. The complexity of securing every measurement against BDI necessitates a strategic approach wherein neither the defender can protect all measurements nor the attacker can compromise them all. This situation is effectively articulated through a two-player zero-sum game, where both entities seek to optimize their strategic interests concerning measurement attacks and defenses.

Through simulations on the PJM 5-Bus system, the paper demonstrates the efficacy of this game-theoretical model in predicting and mitigating the outcomes of BDI attacks. Key numerical results illustrate the dynamic interactions between attackers and defenders, highlighting how optimal strategy formulations can help manage the threat of BDIs in real-time markets. The game's equilibrium strategies suggest proportions with which measurements should be attacked or defended, providing a practical framework for resource allocation in grid security.

Notably, the paper analyzes the implications of potential price deviations due to BDI, reinforcing the importance of robust cybersecurity measures in protecting market operations. From a theoretical standpoint, this paper provides insights into the intersection of cyber-attacks and market operations within power systems, while practically, it suggests strategic defense mechanisms that can be employed to safeguard critical infrastructure.

Looking forward, future research could explore the incorporation of more sophisticated attack strategies, including coordinated multi-attack models, and the development of advanced defense frameworks employing machine learning techniques for real-time anomaly detection. Furthermore, expanding the model to accommodate complex, large-scale grid systems can enhance its applicability, ensuring the resilience of smart grids against emerging cyber threats. The pursuit of such advancements will be crucial as smart grid technologies continue to evolve and scale.