Tianyue Luo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Tianyue Luo has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Jingzheng Wu
Institute of Software, The Chinese Academy of Sciences, Beijing, China
3 shared publications - Xiang Ling
Institute of Software, Chinese Academy of Sciences, Beijing, China
2 shared publications - Yanjun Wu
Institute of Software, Chinese Academy of Sciences
2 shared publications - Zhiqing Rui
Institute of Software Research Center, Institute of Software, Chinese Academy of Sciences, Beijing, China
2 shared publications - Bin Wu
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
1 shared publication - Bolin Zhou
Institute of Software, Chinese Academy of Sciences, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 17 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 604-615
Version-level Third-Party Library Detection in Android Applications via Class Structural Similarity
- 17 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 604-615
Open AccessVersion-level Third-Party Library Detection in Android Applications via Class Structural Similarity
- Open Access
- 14 April 2024
- Published by Association for Computing Machinery (ACM)
- p. 459-471
When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?
- 14 April 2024
- Published by Association for Computing Machinery (ACM)
- p. 459-471
Open AccessWhen Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?
- Open Access
- 1 January 2020
- Published by Springer Nature
- p. 3-15
PassEye: Sniffing Your Password from HTTP Sessions by Deep Neural Network
Passwords are the most widely used method for user authentication in HTTP websites. Password sniffing attacks are considered a common way to steal password. However, most existing methods have many deficiencies in versatility and automation, such as manual analysis, keyword matching, regular expression and SniffPass. In this paper, to better describe the problem, we propose a HTTP Sessions Password Sniffing (HSPS) attack model which is more suitable in HTTP environment. Furthermore, we propose PassEye, a novel deep neural networkbased implementation of HSPS attack. PassEye is a binary neural network classifier that learns features from the HTTP sessions and identifies Password Authentication Session (PAS). We collected 979,681 HTTP sessions from the HTTP and HTTPS websites for training the binary classifier. The results show that PassEye is effective in sniffing the passwords with an accuracy of 99.38%. In addition, several measures are provided to prevent HSPS attacks in the end.
- 1 January 2020
- Published by Springer Nature
- p. 3-15
Open AccessPassEye: Sniffing Your Password from HTTP Sessions by Deep Neural Network
Passwords are the most widely used method for user authentication in HTTP websites. Password sniffing attacks are considered a common way to steal password. However, most existing methods have many deficiencies in versatility and automation, such as manual analysis, keyword matching, regular expression and SniffPass. In this paper, to better describe the problem, we propose a HTTP Sessions Password Sniffing (HSPS) attack model which is more suitable in HTTP environment. Furthermore, we propose PassEye, a novel deep neural networkbased implementation of HSPS attack. PassEye is a binary neural network classifier that learns features from the HTTP sessions and identifies Password Authentication Session (PAS). We collected 979,681 HTTP sessions from the HTTP and HTTPS websites for training the binary classifier. The results show that PassEye is effective in sniffing the passwords with an accuracy of 99.38%. In addition, several measures are provided to prevent HSPS attacks in the end.