基于SAE-SV M的CPS攻击检测

信息物理系统(CPS)在工业控制和关键基础设施等领域被广泛应用,由于具有易受攻击的特点,CPS的安全问题变得尤为重要.为了提高CPS攻击检测的准确度,提出一种稀疏自动编码器(SAE)与支持向量机(SVM)结合的攻击检测算法.针对CPS中数据维数高的问题,使用SAE对数据进行特征学习与降维处理,以无监督方法重建新的特征表示;在此基础上以建立一种优化的检测模型为目标,利用改进细菌觅食算法(IBFA)优化SVM的参数.采用田纳西-伊士曼(TE)过程模型为仿真基础,模拟CPS受到恶意攻击的情况,并用提出的算法进行检测.结果表明,所提算法可以有效检测到攻击的发生,并缩短检测时间,提高了CPS的安全性能.

CPS attack detection based on SAE-SVM

Cyber-physical system (CPS) is widely used in industrial control and critical infrastructures. Because of its vulnerability, the security of CPS is especially important. In order to improve the accuracy of CPS attack detection, an attack detection method combining sparse autoencoder (SAE) and support vector machine (SVM) is proposed in this paper. For the purpose of dimension reduction of data in CPS, SAE is used to learn and reduce dimension of the data, and the unsupervised method is adopted to reconstruct the new representation of features. On this basis, in order to establish an optimized detection model, improved bacterial foraging algorithm(IBFA) is employed to optimize parameters of SVM. The Tennessee-Eastman process model is utilized as simulation foundation to simulate a malicious attack to CPS, and the proposed algorithm is then used to detect the attack. Results coming out of above simulation and detections indicate that the proposed algorithm can detect occurrence of attacks effectively, which not only shorten detection time but also improve security performance of CPS.