Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms

Abstract

Supervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids' performance and safety. The lack of algorithms protecting the power system communication protocols makes them vulnerable to cyberattacks, which can result in a hacker introducing false data into the operational network. This can result in delayed attack detection, which might harm the infrastructure, cause financial loss, or even result in fatalities. Similarly, attackers may be able to feed the system with fake information to hoax the operator and the algorithm into making bad decisions at crucial moments. This paper attempts to identify and classify such cyber-attacks by using numerous deep learning algorithms and optimizing the data features with a metaheuristic algorithm. We proposed a Restricted Boltzmann Machine-based nature-inspired artificial root foraging optimization algorithm. Using a publicly available dataset produced in Mississippi State University's Oak Ridge National Laboratory, simulations are run on the Jupiter Notebook. Traditional supervised machine learning algorithms like Artificial Neural Networks, Convolutional Neural Networks, and Support Vector Machines are measured with the proposed algorithm to demonstrate the effectiveness of the algorithms. Simulations show that the proposed algorithm produced superior results, with an accuracy of 97.8% for binary classification, 95.6% for three-class classification, and 94.3% for multi-class classification. Thereby outperforming its counterpart algorithms in terms of accuracy, precision, recall, and f1 score.

Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

“…DL algorithms, unlike shallow ML algorithms, possess the advantage of automatic feature extraction without the need for explicit feature engineering. Numerous studies [18], [20], [31], [33], [43]- [47] consistently demonstrate the promising potential of DL, consistently showcasing its superiority over shallow ML algorithms. This superiority is reflected in the higher detection accuracy achieved by DL models, making them a crucial and effective approach for detecting malicious power consumption readings.…”

“…Various statistical and analytical techniques have been proposed as countermeasures against electricity theft attacks. These methodologies utilize various approaches such as metaheuristic methods [32], [33], game theory [34]- [36], data mining, state estimation [37], clustering, principal component analysis (PCA), and local outlier factor (LOF). For instance, Singh et al [38] propose an innovative approach that employs PCA to detect anomalies by calculating an anomaly score and comparing it to a predefined threshold.…”

“…These detectors can be categorized into two main groups. The first group comprises detectors that utilize shallow ML detection algorithms such as decision trees (DTs), logistic regression (LR), Naïve Bayes, autoregressive integrated moving average (ARIMA), and support vector machines (SVM) [13], [14], [17], [33], [41], [42]. The second group consists of detectors that leverage DL detection algorithms [18], [20], [31].…”

Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations

“…Dagoumas [ 80 ] has used IEEE RTS 96 power system, and the author highlighted that a combination of operating conditions and cyber-attacks should be used to evaluate system stability. Diaba et al [ 81 ] highlighted that power system communication protocols are prone to cyber-attacks by hackers. The authors have proposed an algorithm outperforming conventional deep learning approaches using SVM, ANN, and CNN.…”