基于SOS-LSTM的核电站隐蔽攻击方法研究  

Research on Covert Attack Method in Nuclear Power Plant Based on SOS-LSTM

作　　者：王东风[1] 张雄 黄宇[1] 邓鉴湧 郭峰 WANG Dongfeng;ZHANG Xiong;HUANG Yu;DENG Jianyong;GUO Feng(Department of Automation,North China Electric Power University,Baoding 071003,Hebei Province,China;Shenhua Science and Technology Development Co.,Ltd.,Beijing 100039,China)

机构地区：[1]华北电力大学自动化系,河北保定071003 [2]神华科技发展有限责任公司,北京100039

出　　处：《动力工程学报》2024年第6期930-938,共9页Journal of Chinese Society of Power Engineering

基　　金：中央高校基本科研业务费专项资金资助项目(2021MS089)。

摘　　要：针对实现隐蔽攻击需要获取攻击目标高精度估计模型的问题,提出一种基于共生生物搜索算法优化长短期记忆神经网络(SOS-LSTM)的隐蔽攻击方法。首先,将攻击目标的反馈控制器输出和输入信号作为长短期记忆神经网络的数据集,通过训练得到受攻击区域的估计模型,再利用估计模型设计隐蔽攻击器向受攻击对象施加攻击信号。此外,使用SOS算法优化LSTM的网络参数来提升隐蔽攻击器的性能。对核电站一回路控制系统进行隐蔽攻击的仿真实验结果表明,该攻击方法在对目标控制系统输出信号实现预先设定攻击行为的同时具有较高隐蔽性。A covert attack method based on a symbiotic organism search(SOS)algorithm to optimize long short-term memory(LSTM)neural network was proposed to solve the problem of obtaining a high-precision estimation model of the attacked target for covert attacks.The output and input signals of the feedback controller of the attack target were taken as the data set of the LSTM.The estimation model of the attacked area was obtained through training,and was used to design the covert attacker to impose attack signals on the attacked object.In addition,the SOS algorithm was applied to optimize the parameters of the LSTM to improve the performance of the covert attacker.The simulation results of covert attack on the primary circuit control system of nuclear power plant show that the attack method has high concealment performance while realizing preset attack behavior on the output signal of the target control system.

关 键 词：核电站 一回路控制系统 隐蔽攻击 共生生物搜索算法 长短期记忆神经网络 

分 类 号：TM623[电气工程—电力系统及自动化] TP273[自动化与计算机技术—检测技术与自动化装置]