Dynamic TLS Fingerprints for Decryption-Free Attack Detection
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Solution Overview
Problem
Existing security protection devices struggle to accurately detect attack traffic in TLS data streams due to the low accuracy of preset fingerprint databases, leading to inefficient resource usage and potential privacy breaches during decryption.
Innovation Solution
A method for detecting attack traffic using dynamically generated fingerprints based on packet fields of TLS data streams, allowing real-time detection without decryption, and updating these fingerprints to adapt to changing attack patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a preset fingerprint database is used to detect attack traffic, then the detection process is simple, but the detection accuracy is low
Solution Approach 1:
The patent transforms the static preset fingerprint database into a dynamic fingerprint generation system. The security protection device generates fingerprints in real-time based on actual traffic characteristics, allowing the detection system to adapt to evolving attack patterns while maintaining operational simplicity through automated dynamic updates.
Solution Approach 2:
The system implements feedback mechanisms where detection results and traffic patterns are continuously analyzed to refine and update fingerprints. This feedback loop enables the system to learn from actual attack traffic and improve detection accuracy over time while keeping the detection process straightforward for operators.
2Reliability
If TLS data streams are decrypted for analysis, then detection thoroughness is improved, but computing resources are consumed and user privacy is compromised
Solution Approach 1:
The patent extracts only the essential fingerprint characteristics from TLS data streams without performing full decryption. By taking out only the necessary identifying features needed for attack detection, the system achieves thorough detection while minimizing computing resource consumption and preserving user privacy.
Solution Approach 2:
Instead of decrypting and analyzing the actual encrypted data, the system creates and analyzes fingerprint copies or representations of the traffic characteristics. This approach maintains detection thoroughness by analyzing traffic patterns while avoiding the computational overhead and privacy concerns associated with full decryption.
3Reliability
If TLS data streams are decrypted for analysis, then detection thoroughness is improved, but user privacy security is compromised
Solution Approach 1:
The system extracts only the minimal necessary fingerprint information from TLS traffic for detection purposes, leaving the actual encrypted content intact and private. This extraction approach ensures detection thoroughness while preserving user privacy security by not exposing sensitive data.
Solution Approach 2:
The patent introduces fingerprints as an intermediary representation between the encrypted traffic and the detection system. This intermediary layer enables thorough detection analysis without requiring direct access to or decryption of the actual user data, thereby maintaining privacy security while achieving detection goals.
Data Source
AI summary
This application discloses a method for detecting attack traffic and a related device. The method may be applied to a security protection device. The security protection device obtains a first rate representation value of first traffic in a first time period, where the first traffic includes at least one first data stream, and destination IP addresses of all first data streams are the same, or a destination IP address of the at least one first data stream belongs to one IP group. Then, the security protection device generates at least one fingerprint based on the first rate representation value, where each fingerprint is generated based on a packet field of one of the at least one first data stream, and any fingerprint is used to detect whether a data stream that matches the any fingerprint is attack traffic. The method can improve detection accuracy of attack traffic.