Covert Cyber Assault Detection in Smart Grid Networks Utilizing Feature Selection and Euclidean Distance-Based Machine Learning

Abstract

Communications technologies are an integral part of efficient monitoring and reliable control in smart grids, but enhanced reliance on these technologies heightens the risk of cyber assaults. Recently, a new type of stealth, or covert, assault in smart grid networks has been discovered, which cannot be ascertained by legacy bad-data detectors using state estimation. Due to the delay-sensitive nature of smart grid networks, swift detection of abnormal changes is immensely desired. In this paper, we propose two Euclidean distance-based anomaly detection schemes for covert cyber-assault detection in smart grid communications networks. The first scheme utilizes unsupervised-learning over unlabeled data to detect outliers or deviations in the measurements. The second scheme employs supervised-learning over labeled data to detect the deviations in the measurements. Unlike the classic detection test, the proposed schemes tackle an unknown sample with low computational complexity, leading to a shorter decision time. To improve detection accuracy and further reduce the computational complexity and the associated time delay, we employ a genetic algorithm-based feature selection method to choose the distinguishing optimal feature data subset as input to both of the proposed schemes. The evaluation is carried out through the standard IEEE 14-bus, 39-bus, 57-bus and 118-bus test systems. Simulation results show that compared to the existing feature extraction-based detection schemes, the proposed schemes show significant improvement in covert cyber deception assault-detection accuracy.

Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection

“…These parameters are stated in table I. [15] and [16]. The resultant subset of features selected by each algorithm are tested with the three classification algorithms, SVM, KNN, and ANN, and their classification accuracy on each of the three IEEE bus systems are recorded in tables III, IV, and V.…”

“…2) Genetic Algorithm: GA is an optimization technique that yields the best solution based on the evolution mechanism of living beings [16]. Following the principle of natural selection, GA chooses the best solutions based on their fitness.…”

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

“…Load distribution attack, stealthy deception attack, covert cyber deception attack, data integrity attack, and malicious data attack -all these terms are also used to mention such attacks [196], [197]. FDIAs need to be capable of escaping bad data detection (BDD) protocols in place, and perform stealth attacks on the system state estimation mechanism [196] -which is fundamental to monitor the state of a power system [197]. Also, most of the legacy BDD systems fail to detect such attacks [197].…”

“…FDIAs need to be capable of escaping bad data detection (BDD) protocols in place, and perform stealth attacks on the system state estimation mechanism [196] -which is fundamental to monitor the state of a power system [197]. Also, most of the legacy BDD systems fail to detect such attacks [197]. Along with affecting the state estimations, FDIA can disrupt electricity markets through false economic dispatch and data [196], [198], [199].…”

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

“…Thus, the time consumed in detection can be saved; however, all measurement samples are reconstructed in this scheme, whether they were attacked or not. To extend our previous work from detection [6,25,26] to mitigation, in this paper, we propose a deep neural network (DNN)-based data reconstruction scheme (Scheme-III) to mitigate the impacts of a CCDA on the SG's measurement dataset.…”