基于机器学习的电力互联网攻击信息识别方法研究

Research on the method of power Internet attack information recognition based on machine learning

为解决传统攻击信息识别方法存在识别误差大的问题,提出基于机器学习的电力互联网攻击信息识别方法。依据互联网攻击信息,构建互联网攻击信息模型,分析基于机器学习的电力互联网攻击信息识别原理,结合哈希定值保障相同攻击信息会分配到同一线程之中,避免噪声产生的偏差,实现电力互联网攻击信息的实时无损处理。构建脆弱性邻接矩阵,并对脆弱性进行定量评估,完成电力互联网攻击信息优化识别方案设计。实验结果表明,该方法识别精度最高可达到98%,能够有效降低电力互联网网络攻击风险,保障网络安全稳定运行。

In order to solve the problem of large error in traditional attack information recognition methods,a machine learning based attack information recognition method for power Internet is proposed.According to the Internet attack information,the Internet attack information model is constructed,the principle of power Internet attack information recognition based on machine learning is analyzed,and the same attack information will be allocated to the same thread with hash fixed value guarantee,so as to avoid the deviation caused by noise and realize the real-time lossless processing of power Internet attack information.The vulnerability adjacency matrix is constructed,and the vulnerability is quantitatively evaluated to complete the design of optimal identification scheme of power Internet attack information.The experimental results show that the recognition accuracy of this method is up to 98%,which can effectively reduce the risk of power Internet network attack and ensure the safe and stable operation of the network.