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Xiaolei Liu
Researcher at China Academy of Engineering Physics
Publications - 37
Citations - 389
Xiaolei Liu is an academic researcher from China Academy of Engineering Physics. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 7, co-authored 30 publications receiving 195 citations. Previous affiliations of Xiaolei Liu include University of Electronic Science and Technology of China.
Papers
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Journal ArticleDOI
Transaction-based classification and detection approach for Ethereum smart contract
Teng Hu,Teng Hu,Xiaolei Liu,Ting Chen,Xiaosong Zhang,Xiaoming Huang,Niu Weina,Jiazhong Lu,Kun Zhou,Kun Zhou,Yuan Liu +10 more
TL;DR: This paper collected over 10,000 smart contracts from Ethereum and focused on the data behavior generated by smart contracts and users, and proposed a transaction-based classification and detection approach for Ethereum smart contract to address issues.
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Adversarial Samples on Android Malware Detection Systems for IoT Systems.
TL;DR: In this article, the authors proposed a testing framework for learning-based Android malware detection systems (TLAMD) for IoT devices, which can generate adversarial samples for the IoT Android application with a success rate of nearly 100%.
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An Insider Threat Detection Approach Based on Mouse Dynamics and Deep Learning
TL;DR: This paper proposes a user authentication method based on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity authentication on current computer users, thus to address insider threats.
Journal ArticleDOI
Adversarial Samples on Android Malware Detection Systems for IoT Systems
TL;DR: A testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices is proposed that can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.
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Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment
TL;DR: The experimental results show that the evolutionary optimization algorithm based on improved t-distribution can effectively deal with the problems of high computational complexity and low search efficiency encountered in UAV dynamic track planning.