Biblio

Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.

As a decoy for hackers, honeypots have been proved to be a very valuable tool for collecting real data. However, due to closed source and vendor-specific firmware, there are significant limitations in cost for researchers to design an easy-to-use and high-interaction honeypot for industrial control systems (ICSs). To solve this problem, it’s necessary to find a cost-effective solution. In this paper, we propose a novel honeypot architecture termed HoneyVP to support a semi-virtual and semi-physical honeypot design and implementation to enable high cost performance. Specially, we first analyze cyber-attacks on ICS devices in view of different interaction levels. Then, in order to deal with these attacks, our HoneyVP architecture clearly defines three basic independent and cooperative components, namely, the virtual component, the physical component, and the coordinator. Finally, a local-remote cooperative ICS honeypot system is implemented to validate its feasibility and effectiveness. Our experimental results show the advantages of using the proposed architecture compared with the previous honeypot solutions. HoneyVP provides a cost-effective solution for ICS security researchers, making ICS honeypots more attractive and making it possible to capture physical interactions.

This thesis focuses on the detection of malware in industrial Internet. The basic flow of the detection of malware contains feature extraction and sample identification. API graph can effectively represent the behavior information of malware. However, due to the high algorithm complexity of solving the problem of subgraph isomorphism, the efficiency of analysis based on graph structure feature is low. Due to the different scales of API graph of different malicious codes, the API graph needs to be normalized. Considering the difficulties of sample collection and manual marking, it is necessary to expand the number of malware samples in industrial Internet. This paper proposes a method that combines PageRank with TF-IDF to process the API graph. Besides, this paper proposes a method to construct the adversarial samples of malwares based on GAN.