Extracting Encrypted Network Traffic via Browser Session Key Logging
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Solution Overview
Problem
Current methods for extracting specific information from network traffic, particularly sensitive data like login credentials and session cookies, face challenges due to the complexity of modern web technologies and the prevalence of encryption, with existing solutions often limited to either encrypted or non-encrypted traffic and lacking in reliability and efficiency.
Innovation Solution
A system and method for extracting information from both encrypted and non-encrypted network traffic using the DESEK process, which exploits browser features to log SSL/TLS session keys, allowing for decryption and extraction of sensitive data such as visited URLs, login credentials, and Facebook activities, utilizing tools like SSLKeyExtractor and NetInfoMiner to process and summarize the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If encryption is used to protect network traffic, then security and privacy are improved, but ability to extract sensitive information deteriorates
Solution Approach 1:
The system performs preliminary actions by capturing network traffic before encryption takes effect, or by capturing traffic during the encryption setup phase where session keys are established. This allows the system to have access to encryption keys or unencrypted data before the encryption fully protects the communication, enabling subsequent extraction of sensitive information while maintaining the security benefits of encryption.
Solution Approach 2:
The system introduces an intermediary approach by acting as a traffic analyzer that can access both encrypted traffic and the corresponding session keys through browser features. This intermediary capability allows the system to decrypt and analyze sensitive information without compromising the overall security architecture, effectively mediating between encryption protection and information extraction needs.
2Device complexity
If existing extraction methods are used, then simplicity is maintained, but reliability and efficiency deteriorate due to limited functionality and single-traffic-type support
Solution Approach 1:
The system implements universality by designing a single extraction framework that can handle multiple traffic types (encrypted HTTPS, unencrypted HTTP, and WebSocket traffic) and extract various information types (URLs, credentials, cookies, social network activities). This multi-functional approach eliminates the need for separate simple tools for each traffic type, thereby improving reliability without sacrificing operational simplicity.
Solution Approach 2:
The system applies segmentation by dividing the extraction process into distinct modules: traffic capture, decryption (when applicable), information extraction, and result summarization. This segmented architecture allows each component to be optimized independently, maintaining overall simplicity while improving reliability through specialized processing of different traffic types and information types.
3Loss of information
If deep analysis of large volumes of network traffic is performed, then information extraction completeness is improved, but processing capability and time requirements deteriorate
Solution Approach 1:
The system extracts only the most relevant and sensitive information from large volumes of network traffic using targeted detection patterns and heuristics. Instead of analyzing every packet in detail, the system extracts specific information types (URLs, credentials, cookies, social activities) that are most valuable for digital forensics and penetration testing, thereby maintaining high information completeness for critical data while improving processing capability.
Solution Approach 2:
The system applies partial action by focusing analysis on specific portions of network traffic that are most likely to contain valuable information, such as authentication requests, cookie exchanges, and social network API calls. This selective approach ensures comprehensive extraction of important information while avoiding the time-consuming analysis of all traffic data, thereby improving productivity.
4Adaptability or versatility
If multiple protocols and traffic types are supported, then versatility is improved, but system complexity and implementation difficulty deteriorate
Solution Approach 1:
The system achieves versatility by implementing a universal traffic analysis framework that can handle multiple protocols (HTTP, HTTPS, WebSocket) and extract various information types through a common processing architecture. This unified approach allows the system to support diverse traffic types without requiring separate complex implementations for each protocol, thereby improving versatility while managing system complexity through standardized processing patterns.
Data Source
AI summary
A system and method are described for information extraction from network traffic traces that are both encrypted and non-encrypted. The system includes a client computer and a remote computer, where the client computer communicates data over a network. The client computer sets a session key log file environment variable, such that when the client computer launches a supported browser, a session key log file (KLF) is created, computer network traffic traces are captured by retrieving data from encrypted traffic, and the KLF and captured traffic are periodically transferred to a remote server. A remote computer performs traffic mining to analyze the captured traffic traces and extract sensitive pieces of information.