%0 Journal Article
%T 基于MIKPSO-SVM方法的工业控制系统入侵检测<br>Intrusion detection for industrial control systems based on an improved SVM method
%A 陈冬青
%A 张普含
%A 王华忠
%J 清华大学学报（自然科学版）
%D 2018
%R 10.16511/j.cnki.qhdxxb.2018.25.019
%X 针对Kalman粒子群算法在优化基于支持向量机的工业控制系统入侵检测模型时易陷入局部极小的问题，该文提出了一种改进的多新息Kalman粒子群算法。所提算法不仅考虑当前粒子信息的观测值，同时充分利用之前时刻的有用信息对粒子的状态进行估计，为粒子位置的更新提供足够的冲量，使得算法跳出局部极小，从而提高了算法的优化精度。将所提出的改进算法用于支持向量机工控入侵检测模型参数寻优，并使用工控入侵检测标准数据集进行仿真研究。仿真结果表明：与Kalman粒子群、粒子群以及遗传算法相比，该文所提出的算法——优化的支持向量机入侵检测模型在检测率、漏报率和误报率等指标上都有明显提升。<br>Abstract：Industrial control system intrusion detection models based on the support vector machine (SVM) optimized by Kalman particle swarm optimization (KPSO) can become trapped in a local minimum. This paper presents a multi-innovation theory based KPSO that not only considers the current time observation information, but also uses previously useful information for predicting the particle states. Therefore, the algorithm provides sufficient momentum for updating the particle position so that the algorithm can jump out of a local minimum for better optimization accuracy. The algorithm was used to optimize the parameters for an SVM based intrusion detection model with the simulation results evaluated using the industrial intrusion detection standard dataset. The results show that the detection rate, false negative rate and false positive rate are significantly better with the SVM intrusion detection model optimized by this algorithm than with the KPSO, PSO and genetic algorithms.
%K 工业控制系统
%K 入侵检测
%K 多新息Kalman粒子群算法
%K 支持向量机
%K &lt
%K br&gt
%K industry control system
%K intrusion detection
%K multi-innovation Kalman particle swarm optimization (MIKPSO)
%K support vector machine (SVM)
%U http://jst.tsinghuajournals.com/CN/Y2018/V58/I4/380