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Song, Yufei, Yu, Zongchao, Liu, Xuan, Tian, Jianwei, CHEN, Mu.  2019.  Isolation Forest based Detection for False Data Attacks in Power Systems. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :4170—4174.
Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.
Chen, Lu, Ma, Yuanyuan, SHAO, Zhipeng, CHEN, Mu.  2019.  Research on Mobile Application Local Denial of Service Vulnerability Detection Technology Based on Rule Matching. 2019 IEEE International Conference on Energy Internet (ICEI). :585–590.
Aiming at malicious application flooding in mobile application market, this paper proposed a method based on rule matching for mobile application local denial of service vulnerability detection. By combining the advantages of static detection and dynamic detection, static detection adopts smali abstract syntax tree as rule matching object. This static detection method has higher code coverage and better guarantees the integrity of mobile application information. The dynamic detection performs targeted hook verification on the static detection result, which improves the accuracy of the detection result and saves the test workload at the same time. This dynamic detection method has good scalability, can be upgraded with discovery and variants of the vulnerability. Through experiments, it is verified that the mobile application with this vulnerability can be accurately found in a large number of mobile applications, and the effectiveness of the system is verified.