基于深度学习的电动汽车充电站SCADA系统勒索软件检测
Ransomware Detection Using Deep Learning in the SCADA System of Electric Vehicle Charging Station
摘要：
监控和数据采集（SCADA）系统一直在不断利用网络架构、通信协议、下一代通信技术（5G、6 G、Wi-Fi 6）和物联网（IoT）的发展。然而，SCADA系统已成为勒索软件攻击者最有利可图和最具吸引力的目标。针对SCADA控制电动汽车充电站（EVCS），提出了基于深度学习的新型勒索软件检测框架，分析了深度神经网络（DNN）、一维卷积神经网络（CNN）和长短期记忆（LSTM）递归神经网络3种深度学习算法的性能。所有三个基于深度学习的模拟框架都实现了约97%的平均准确率（ACC），超过98%的平均曲线下面积（AUC），以及10倍分层交叉验证下的平均F1得分，平均误报率（FAR）小于1.88%。勒索软件驱动的分布式拒绝服务（DDoS）攻击往往通过超过SOC控制阈值来改变SOC配置文件。已发现严重性随着攻击进展和渗透的增加而增加。此外，勒索软件驱动的虚假数据注入（FDI）攻击有可能通过操纵SOC控制阈值来损坏整个BES或物理系统。这是一个设计选择和优化问题，深度学习算法可以基于性能指标之间的权衡来部署。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
