Attack detection and localization in smart grid with image-based deep learning

Smart grid's objective is to enable electricity and information to flow two-way while providing effective, robust, computerized, and decentralized energy delivery. This necessitates the use of state estimation-based techniques and real-time analysis to ensure that effective controls are deployed properly. However, the reliance on communication technologies makes such systems susceptible to sophisticated data integrity attacks imposing serious threats to the overall reliability of smart grid. To detect such attacks, advanced and efficient anomaly detection solutions are needed. In this paper, a two-stage deep learning-based framework is carefully designed by embedding power system's characteristics enabling precise attack detection and localization. First, we encode temporal correlations of the multivariate power system time-series measurements as 2D images using image-based representation approaches such as Gramian Angular Field (GAF) and Recurrence Plot (RP) to obtain the latent data characteristics. These images are then utilized to build a highly reliable and resilient deep Convolutional Neural Network (CNN)-based multi-label classifier capable of learning both low and high level characteristics in the images to detect and discover the exact attack locations without leveraging any prior statistical assumptions. The proposed method is evaluated on the IEEE 57-bus system using real-world load data. Also, a comparative study is carried out. Numerical results indicate that the proposed multi-class cyber-intrusion detection framework outperforms the current conventional and deep learning-based attack detection methods

Ensuring cybersecurity of smart grid against data integrity attacks under concept drift

For achieving increasing artificial intelligence in future smart grids, a very precise state estimation (SE) is required as a prerequisite for many other key functionalities for successful monitoring and control. With increasing interconnection of utility network and internet, traditional state estimators are vulnerable to complex data integrity attacks, such as false data injection (FDI), bypassing existing bad data detection (BDD) schemes. While researchers propose detectors for FDI, such countermeasures neglect power state changes due to contingencies. As such an abrupt physical change negatively affects existing FDI detectors, they will provide incorrect classification of the new instances. To resolve the problem, we conducted analysis for a fundamental understanding of the differences between a physical grid change and data manipulation change. We use outage as an example and propose to analyze historical data followed by concept drift, focusing on distribution change. The key is to find critical lines to narrow down the scope. Techniques such as dimensionality reduction and statistical hypothesis testing are employed. The proposed approach is evaluated on the IEEE 14 bus system using load data from the New York independent system operator with two different attack scenarios: (1) attacks without concept drift, (2) attacks under concept drift. Numerical results show that the new method significantly increases the accuracy of the existing detection methods under concept drift

Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

The expansion of power systems over large geographical areas renders centralized processing inefficient. Therefore, the distributed operation is increasingly adopted. This work introduces a new type of attack against distributed state estimation of power systems, which operates on inter-area boundary buses. We show that the developed attack can circumvent existing robust state estimators and the convergence-based detection approaches. Afterward, we carefully design a deep learning-based cyber-anomaly detection mechanism to detect such attacks. Simulations conducted on the IEEE 14-bus system reveal that the developed framework can obtain a very high detection accuracy. Moreover, experimental results indicate that the proposed detector surpasses current machine learning-based detection methods. Water Resource