Biblio

Filters: Author is Li, Zhihui  [Clear All Filters]
2020-04-03
Zhao, Hui, Li, Zhihui, Wei, Hansheng, Shi, Jianqi, Huang, Yanhong.  2019.  SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective. 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST). :59—67.

Industrial networks are the cornerstone of modern industrial control systems. Performing security checks of industrial communication processes helps detect unknown risks and vulnerabilities. Fuzz testing is a widely used method for performing security checks that takes advantage of automation. However, there is a big challenge to carry out security checks on industrial network due to the increasing variety and complexity of industrial communication protocols. In this case, existing approaches usually take a long time to model the protocol for generating test cases, which is labor-intensive and time-consuming. This becomes even worse when the target protocol is stateful. To help in addressing this problem, we employed a deep learning model to learn the structures of protocol frames and deal with the temporal features of stateful protocols. We propose a fuzzing framework named SeqFuzzer which automatically learns the protocol frame structures from communication traffic and generates fake but plausible messages as test cases. For proving the usability of our approach, we applied SeqFuzzer to widely-used Ethernet for Control Automation Technology (EtherCAT) devices and successfully detected several security vulnerabilities.

2017-09-19
Xie, Lanchi, Xu, Lei, Zhang, Ning, Guo, Jingjing, Yan, Yuwen, Li, Zhihui, Li, Zhigang, Xu, Xiaojing.  2016.  Improved Face Recognition Result Reranking Based on Shape Contexts. Proceedings of the 2016 International Conference on Intelligent Information Processing. :11:1–11:6.

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.