论文标题
智能电网中的机器学习，探测和缓解网络攻击：一项调查
Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Survey
论文作者
论文摘要
Smart Grid（SG）是一个复杂的网络物理系统，它利用现代的网络和物理设备在最佳操作点运行。网络攻击是面临最先进系统的使用和进步面临的主要威胁。 SG的进步增加了广泛的技术，设备和工具，以使系统更可靠，高效且具有成本效益。尽管达到了这些目标，但由于网络网络的广泛实施，对抗攻击的威胁空间也得到了扩展。由于有希望的计算能力和推理能力，机器学习（ML）分别被攻击者和系统操作员分别用于利用和捍卫SG中的网络攻击。在本文中，我们通过审查SG域中的最新研究，对网络攻击生成，检测和缓解方案进行全面摘要。此外，我们使用表格格式以结构化的方式总结了当前的研究。我们还根据调查提出了现有作品的缺点以及可能的未来研究方向。
Smart grid (SG) is a complex cyber-physical system that utilizes modern cyber and physical equipment to run at an optimal operating point. Cyberattacks are the principal threats confronting the usage and advancement of the state-of-the-art systems. The advancement of SG has added a wide range of technologies, equipment, and tools to make the system more reliable, efficient, and cost-effective. Despite attaining these goals, the threat space for the adversarial attacks has also been expanded because of the extensive implementation of the cyber networks. Due to the promising computational and reasoning capability, machine learning (ML) is being used to exploit and defend the cyberattacks in SG by the attackers and system operators, respectively. In this paper, we perform a comprehensive summary of cyberattacks generation, detection, and mitigation schemes by reviewing state-of-the-art research in the SG domain. Additionally, we have summarized the current research in a structured way using tabular format. We also present the shortcomings of the existing works and possible future research direction based on our investigation.
