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Zhou, Liang, Ouyang, Xuan, Ying, Huan, Han, Lifang, Cheng, Yushi, Zhang, Tianchen.  2018.  Cyber-Attack Classification in Smart Grid via Deep Neural Network. Proceedings of the 2Nd International Conference on Computer Science and Application Engineering. :90:1–90:5.
Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%.
Y
Ying, Huan, Zhang, Yanmiao, Han, Lifang, Cheng, Yushi, Li, Jiyuan, Ji, Xiaoyu, Xu, Wenyuan.  2019.  Detecting Buffer-Overflow Vulnerabilities in Smart Grid Devices via Automatic Static Analysis. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :813-817.

As a modern power transmission network, smart grid connects plenty of terminal devices. However, along with the growth of devices are the security threats. Different from the previous separated environment, an adversary nowadays can destroy the power system by attacking these devices. Therefore, it's critical to ensure the security and safety of terminal devices. To achieve this goal, detecting the pre-existing vulnerabilities of the device program and enhance the terminal security, are of great importance and necessity. In this paper, we propose a novel approach that detects existing buffer-overflow vulnerabilities of terminal devices via automatic static analysis (ASA). We utilize the static analysis to extract the device program information and build corresponding program models. By further matching the generated program model with pre-defined vulnerability patterns, we achieve vulnerability detection and error reporting. The evaluation results demonstrate that our method can effectively detect buffer-overflow vulnerabilities of smart terminals with a high accuracy and a low false positive rate.

Ying, Huan, Ouyang, Xuan, Miao, Siwei, Cheng, Yushi.  2019.  Power Message Generation in Smart Grid via Generative Adversarial Network. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :790–793.
As the next generation of the power system, smart grid develops towards automated and intellectualized. Along with the benefits brought by smart grids, e.g., improved energy conversion rate, power utilization rate, and power supply quality, are the security challenges. One of the most important issues in smart grids is to ensure reliable communication between the secondary equipment. The state-of-art method to ensure smart grid security is to detect cyber attacks by deep learning. However, due to the small number of negative samples, the performance of the detection system is limited. In this paper, we propose a novel approach that utilizes the Generative Adversarial Network (GAN) to generate abundant negative samples, which helps to improve the performance of the state-of-art detection system. The evaluation results demonstrate that the proposed method can effectively improve the performance of the detection system by 4%.
C
Cheng, Yushi, Ji, Xiaoyu, Lu, Tianyang, Xu, Wenyuan.  2018.  DeWiCam: Detecting Hidden Wireless Cameras via Smartphones. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :1–13.

Wireless cameras are widely deployed in surveillance systems for security guarding. However, the privacy concerns associated with unauthorized videotaping, are drawing an increasing attention recently. Existing detection methods for unauthorized wireless cameras are either limited by their detection accuracy or requiring dedicated devices. In this paper, we propose DeWiCam, a lightweight and effective detection mechanism using smartphones. The basic idea of DeWiCam is to utilize the intrinsic traffic patterns of flows from wireless cameras. Compared with traditional traffic pattern analysis, DeWiCam is more challenging because it cannot access the encrypted information in the data packets. Yet, DeWiCam overcomes the difficulty and can detect nearby wireless cameras reliably. To further identify whether a camera is in an interested room, we propose a human-assisted identification model. We implement DeWiCam on the Android platform and evaluate it with extensive experiments on 20 cameras. The evaluation results show that DeWiCam can detect cameras with an accuracy of 99% within 2.7 s.