9/27/2023 0 Comments Wireshark password sniffing httpsFor examples, attackers listen to the network traffic packets and search for PAS, Keyword matching is also fast and effective, but experiments show that it has a high false-positive rate, and regular expression is an upgraded version of the former two. Because of the diversity of websites, manual analysis is probably the most common and effective measure. HTTP websites usually perform password authentication through sessions. Session is the basic unit of communication between the client and the server in the HTTP protocol including request and response messages. Traditional methods of password sniffing attacks include manual analysis, keyword matching, regular expression and automatic tool, which can attack some HTTP websites. The widespread use of plain text password transmission and weakly encrypted password transmission in HTTP websites makes password sniffing attacks more easily. This gives attackers the opportunity to perform brute force attacks, password sniffing attacks and password reuse attacks. Some more secure methods have been proposed for the same purpose, such as fingerprint, asymmetric key, 2-step verification, one-time password, but password is still the most widely used one due to its convenience, simplicity, and user habits. However, this authentication method has many security problems, which has been criticized for a long time. Password is a traditional identity authentication method. In addition, several measures are provided to prevent HSPS attacks in the end. The results show that PassEye is effective in sniffing the passwords with an accuracy of 99.38%. We collected 979,681 HTTP sessions from the HTTP and HTTPS websites for training the binary classifier. PassEye is a binary neural network classifier that learns features from the HTTP sessions and identifies Password Authentication Session (PAS). Furthermore, we propose PassEye, a novel deep neural networkbased implementation of HSPS attack. 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. However, most existing methods have many deficiencies in versatility and automation, such as manual analysis, keyword matching, regular expression and SniffPass. Password sniffing attacks are considered a common way to steal password. Passwords are the most widely used method for user authentication in HTTP websites.
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