Biblio

In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.

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).

Anomaly detection generally involves the extraction of features from entities' or users' properties, and the design of anomaly detection models using machine learning or deep learning algorithms. However, only considering entities' property information could lead to high false positives. We posit the importance of also considering connections or relationships between entities in the detecting of anomalous behaviors and associated threat groups. Therefore, in this paper, we design a GCN (graph convolutional networks) based anomaly detection model to detect anomalous behaviors of users and malicious threat groups. The GCN model could characterize entities' properties and structural information between them into graphs. This allows the GCN based anomaly detection model to detect both anomalous behaviors of individuals and associated anomalous groups. We then evaluate the proposed model using a real-world insider threat data set. The results show that the proposed model outperforms several state-of-art baseline methods (i.e., random forest, logistic regression, SVM, and CNN). Moreover, the proposed model can also be applied to other anomaly detection applications.