  The Supervisory control and data acquisition (SCADA) systems have been
continuously leveraging the evolution of network architecture, communication
protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the
internet of things (IoT). However, SCADA system has become the most profitable
and alluring target for ransomware attackers. This paper proposes the deep
learning-based novel ransomware detection framework in the SCADA controlled
electric vehicle charging station (EVCS) with the performance analysis of three
deep learning algorithms, namely deep neural network (DNN), 1D convolution
neural network (CNN), and long short-term memory (LSTM) recurrent neural
network. 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 with
an average false alarm rate (FAR) less than 1.88%. Ransomware driven
distributed denial of service (DDoS) attack tends to shift the SOC profile by
exceeding the SOC control thresholds. The severity has been found to increase
as the attack progress and penetration increases. Also, ransomware driven false
data injection (FDI) attack has the potential to damage the entire BES or
physical system by manipulating the SOC control thresholds. It's a design
choice and optimization issue that a deep learning algorithm can deploy based
on the tradeoffs between performance metrics.
