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Huo, Dongdong, Wang, Yu, Liu, Chao, Li, Mingxuan, Wang, Yazhe, Xu, Zhen.  2020.  LAPE: A Lightweight Attestation of Program Execution Scheme for Bare-Metal Systems. 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :78—86.

Unlike traditional processors, Internet of Things (IoT) devices are short of resources to incorporate mature protections (e.g. MMU, TrustZone) against modern control-flow attacks. Remote (control-flow) attestation is fast becoming a key instrument in securing such devices as it has proven the effectiveness on not only detecting runtime malware infestation of a remote device, but also saving the computing resources by moving the costly verification process away. However, few control-flow attestation schemes have been able to draw on any systematic research into the software specificity of bare-metal systems, which are widely deployed on resource-constrained IoT devices. To our knowledge, the unique design patterns of the system limit implementations of such expositions. In this paper, we present the design and proof-of-concept implementation of LAPE, a lightweight attestation of program execution scheme that enables detecting control-flow attacks for bare-metal systems without requiring hardware modification. With rudimentary memory protection support found in modern IoT-class microcontrollers, LAPE leverages software instrumentation to compartmentalize the firmware functions into several ”attestation compartments”. It then continuously tracks the control-flow events of each compartment and periodically reports them to the verifier. The PoC of the scheme is incorporated into an LLVM-based compiler to generate the LAPE-enabled firmware. By taking experiments with several real-world IoT firmware, the results show both the efficiency and practicality of LAPE.

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Zou, Zhenwan, Hou, Yingsa, Yang, Huiting, Li, Mingxuan, Wang, Bin, Guo, Qingrui.  2019.  Research and Implementation of Intelligent Substation Information Security Risk Assessment Tool. 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). :1306–1310.

In order to improve the information security level of intelligent substation, this paper proposes an intelligent substation information security assessment tool through the research and analysis of intelligent substation information security risk and information security assessment method, and proves that the tool can effectively detect it. It is of great significance to carry out research on industrial control systems, especially intelligent substation information security.

Li, Mingxuan, Yang, Zhushi, Zhong, Jinsong, He, Ling, Teng, Yangxin.  2020.  Research on Network Attack and Defense Based on Artificial Intelligence Technology. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2532—2534.
This paper combines the common ideas and methods in offensive and defensive confrontation in recent years, and uses artificial intelligence technology-based network asset automatic mining technology and artificial intelligence technology-based vulnerability automatic exploitation technology, carries out research and specific practices in discovering and using system vulnerability based on artificial intelligence technology, designs and implemented automatic binary vulnerability discovering and exploitation system, which improves improves the efficiency and success rate of vulnerability discovering and exploitation.
Li, Mingxuan, Yang, Zhushi, He, Ling, Teng, Yangxin.  2019.  Research on Typical Model of Network Invasion and Attack in Power Industrial Control System. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:2070–2073.

Aiming at the operation characteristics of power industry control system, this paper deeply analyses the attack mechanism and characteristics of power industry control system intrusion. On the basis of classifying and sorting out the attack characteristics of power industrial control system, this paper also attaches importance to break the basic theory and consequential technologies of industrial control network space security, and constructs the network intrusion as well as attack model of power industrial control system to realize the precise characterization of attackers' attack behavior, which provides a theoretical model for the analysis and early warning of attack behavior analysis of power industrial control systems.

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Yang, Shouguo, Shi, Zhiqiang, Zhang, Guodong, Li, Mingxuan, Ma, Yuan, Sun, Limin.  2019.  Understand Code Style: Efficient CNN-Based Compiler Optimization Recognition System. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–6.
Compiler optimization level recognition can be applied to vulnerability discovery and binary analysis. Due to the exists of many different compilation optimization options, the difference in the contents of the binary file is very complicated. There are thousands of compiler optimization algorithms and multiple different processor architectures, so it is very difficult to manually analyze binary files and recognize its compiler optimization level with rules. This paper first proposes a CNN-based compiler optimization level recognition model: BinEye. The system extracts semantic and structural differences and automatically recognize the compiler optimization levels. The model is designed to be very suitable for binary file processing and is easy to understand. We built a dataset containing 80028 binary files for the model training and testing. Our proposed model achieves an accuracy of over 97%. At the same time, BinEye is a fully CNN-based system and it has a faster forward calculation speed, at least 8 times faster than the normal RNN-based model. Through our analysis of the model output, we successfully found the difference in assembly codes caused by the different compiler optimization level. This means that the model we proposed is interpretable. Based on our model, we propose a method to analyze the code differences caused by different compiler optimization levels, which has great guiding significance for analyzing closed source compilers and binary security analysis.