Biblio

In view of the problem that the intrusion detection method based on One-Class Support Vector Machine (OCSVM) could not detect the outliers within the industrial data, which results in the decision function deviating from the training sample, an anomaly intrusion detection algorithm based on Robust Incremental Principal Component Analysis (RIPCA) -OCSVM is proposed in this paper. The method uses RIPCA algorithm to remove outliers in industrial data sets and realize dimensionality reduction. In combination with the advantages of OCSVM on the single classification problem, an anomaly detection model is established, and the Improved Particle Swarm Optimization (IPSO) is used for model parameter optimization. The simulation results show that the method can efficiently and accurately identify attacks or abnormal behaviors while meeting the real-time requirements of the industrial control system (ICS).

Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits known as hardware Trojans has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention and most approaches assume the existence of a “golden model”. Prior works suggest using reverse-engineering to identify such Trojan-free ICs for the golden model but they did not state how to do this efficiently. In this paper, we propose an innovative and robust reverseengineering approach to identify the Trojan-free ICs. We adapt a well-studied machine learning method, one-class support vector machine, to solve our problem. Simulation results using state-of-the-art tools on several publicly available circuits show that our approach can detect hardware Trojans with high accuracy rate across different modeling and algorithm parameters.