ANDROID

Android security. We employ multiple techniques such as static analysis and machine learning to detect Android malware (ISSTA 2016, IJCNN 2016). With evolutionary algorithms, we conduct a work to evaluate existing anti-malware tools (AsiaCCS 2016, TIFS 2017). Another work has been done, with regard to Android ecosystem, to analyze the spread model of Android malware across multiple markets (TIFS 2019), security analysis of third-party libraries (C&S 2019), and security patches of apps across versions... AI Security and Privacy. We have conducted a comprehensive survey on security and privacy of deep learning systems which has been accepted by IEEE Transactions on Software Engineering (https://arxiv.org/abs/1911.12562). We are also interested in protecting deep learning system from model stealing (USENIX Sec 2021), backdoor and model inversion... We are organizing an issus on "Data-driven Security" for Cybersecurity journal with Dr. Liu Yang, Dr. Ou xinming, and Dr. Xing Xinyu.