Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures - Stephanie Ness (IEEE Access)
Smart grids themselves have emerged as vital structures of the up-to-date practical power systems or electricity networks that incorporate high technologies and information handling. Yet, they are more susceptible to an adversarial attack that can interfere with the critical functions like energy distribution and faults detection. This paper therefore proposes a new alternative to developing a DL and ML framework for identifying adversarial attacks on smart grids. After analyses of the performances of Logistic Regression, Perceptron, Gaussian Naive Bayes and Multi-Layer Perceptron, LSTM network has better results with an accuracy of 99.81%. The suggested framework strengthens smart grid immunity to cyber threats such as DoS attacks, back door injections, and adversarial perturbations while increasing energy distribution stability and security. For enhancing smart grid security, our results emphasize the importance of integration of ML and DL techniques and provide such an understanding of threat environment for future research and development on threat identification.