多源大规模电网的多阶攻击风险感知量化和防御技术

随着电网的规模逐渐扩大，虽然配电网的数字化为电网运营带来了诸多好处，但也增加了严重的网络安全威胁所带来的风险；为了检测不断发展的电力网络中潜在的攻击和漏洞，满足对安全和隐私机制的额外研究需求，需要一个自动化过程来系统地处理大量跨域信息和关联各种网络情报，以正确评估情况；为此提出了基于深度学习和机器学习的电网多阶段攻击感知风险量化和防御技术，通过人工神经网络就检测电网中是否存在攻击，对系统建模攻击防御树进行多阶段上下文攻击风险量化，并且利用查杀链寻找最优路径进行防御，实现电网威胁全自动化感知和防御决策。

Multi-stage Attack Risk Quantification and Defense Techniques for Multi-source Large-scale Power Grids

With the gradual expansion of the power grid, the digitalization of the distribution network has brought numerous benefits to the operation of the power grid. However, it has also increased the risks posed by serious cyber security threats. To detect potential attacks and vulnerabilities, and to meet the additional research needs for security and privacy mechanisms, an automated process is required to systematically process a large amount of cross-domain information and correlate various network intelligence to accurately assess the situation. For this purpose, we propose a power grid multi-stage attack perception risk quantification and defense technology. This technology employs an artificial neural network to detect attacks in the power grid and performs multi-stage context attack risk quantification on the system modeling attack defense tree. Furthermore, the proposed technology utilizes the killing chain to find the optimal path for defense, ultimately achieving a fully automated threat perception and defense decision system for the power grid.