Visible to the public Model-Based Attack Detection and Mitigation for Automatic Generation Control

TitleModel-Based Attack Detection and Mitigation for Automatic Generation Control
Publication TypeJournal Article
Year of Publication2014
AuthorsSridhar, S., Govindarasu, M.
JournalSmart Grid, IEEE Transactions on
Date PublishedMarch
KeywordsAGC, anomaly detection, attack mitigation algorithm, attack resilient control, automatic generation control, critical assets protection, cyber layer, cyber security measures, cyber systems, cyber-attack-resilient control techniques, data integrity, data integrity attacks, electricity market operation, Electricity supply industry, electronic threats, frequency control, Frequency measurement, Generators, host-based security technologies, Intrusion Detection Systems, kernel density estimation, model-based anomaly detection algorithm, model-based mitigation algorithm, network-based security technologies, physical system, Power measurement, power system control, power system frequency, power system operation reliability, power system reliability, power system stability, ramp attacks, scaling attacks, security of data, smart attack detection, smart attack mitigation, supervisory control and data acquisition

Cyber systems play a critical role in improving the efficiency and reliability of power system operation and ensuring the system remains within safe operating margins. An adversary can inflict severe damage to the underlying physical system by compromising the control and monitoring applications facilitated by the cyber layer. Protection of critical assets from electronic threats has traditionally been done through conventional cyber security measures that involve host-based and network-based security technologies. However, it has been recognized that highly skilled attacks can bypass these security mechanisms to disrupt the smooth operation of control systems. There is a growing need for cyber-attack-resilient control techniques that look beyond traditional cyber defense mechanisms to detect highly skilled attacks. In this paper, we make the following contributions. We first demonstrate the impact of data integrity attacks on Automatic Generation Control (AGC) on power system frequency and electricity market operation. We propose a general framework to the application of attack resilient control to power systems as a composition of smart attack detection and mitigation. Finally, we develop a model-based anomaly detection and attack mitigation algorithm for AGC. We evaluate the detection capability of the proposed anomaly detection algorithm through simulation studies. Our results show that the algorithm is capable of detecting scaling and ramp attacks with low false positive and negative rates. The proposed model-based mitigation algorithm is also efficient in maintaining system frequency within acceptable limits during the attack period.

Citation Key6740883