Biblio

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 state of charge (SOC) profile by exceeding the SOC control thresholds. 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.

Ransomware attacks are taking advantage of the ongoing pandemics and attacking the vulnerable systems in business, health sector, education, insurance, bank, and government sectors. Various approaches have been proposed to combat ransomware, but the dynamic nature of malware writers often bypasses the security checkpoints. There are commercial tools available in the market for ransomware analysis and detection, but their performance is questionable. This paper aims at proposing an AI-based ransomware detection framework and designing a detection tool (AIRaD) using a combination of both static and dynamic malware analysis techniques. Dynamic binary instrumentation is done using PIN tool, function call trace is analyzed leveraging Cuckoo sandbox and Ghidra. Features extracted at DLL, function call, and assembly level are processed with NLP, association rule mining techniques and fed to different machine learning classifiers. Support vector machine and Adaboost with J48 algorithms achieved the highest accuracy of 99.54% with 0.005 false-positive rates for a multi-level combined term frequency approach.