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The Supervisory control and data acquisition (SCADA) systems have beencontinuously leveraging the evolution of network architecture, communicationprotocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and theinternet of things (IoT). However, SCADA system has become the most profitableand alluring target for ransomware attackers. This paper proposes the deeplearning-based novel ransomware detection framework in the SCADA controlledelectric vehicle charging station (EVCS) with the performance analysis of threedeep learning algorithms, namely deep neural network (DNN), 1D convolutionneural network (CNN), and long short-term memory (LSTM) recurrent neuralnetwork. All three-deep learning-based simulated frameworks achieve around 97%average accuracy (ACC), more than 98% of the average area under the curve(AUC), and an average F1-score under 10-fold stratified cross-validation withan average false alarm rate (FAR) less than 1.88%. Ransomware drivendistributed denial of service (DDoS) attack tends to shift the SOC profile byexceeding the SOC control thresholds. The severity has been found to increaseas the attack progress and penetration increases. Also, ransomware driven falsedata injection (FDI) attack has the potential to damage the entire BES orphysical system by manipulating the SOC control thresholds. It's a designchoice and optimization issue that a deep learning algorithm can deploy basedon the tradeoffs between performance metrics.

Ransomware Detection Using Deep Learning in the SCADA System of Electric Vehicle Charging Station