TLS Fingerprint Recognition via Finite-State Machine Models
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Solution Overview
Problem
Existing methods for recognizing network protocols, such as those based on transport-layer ports, payloads, and statistical features, are unable to provide fine-grained protocol information and fail to detect TLS vulnerabilities effectively.
Innovation Solution
A system and method utilizing finite-state machines to infer state machine models of TLS implementations, extract multi-level fingerprints, and verify version information, which includes a model inference module, fingerprint extracting module, and version recognizing module to recognize types and versions of TLS implementations with higher accuracy and covertly with fewer data packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods based on transport-layer ports, payloads, and statistical features are used for protocol recognition, then the recognition process is simple, but fine-grained protocol information cannot be provided
Solution Approach 1:
The patent introduces finite-state machine models as an intermediary representation layer between raw network traffic and protocol recognition. These models capture the state transitions and message exchanges of TLS protocols, enabling precise protocol identification and version recognition without requiring complex analysis of raw packets. The finite-state machines serve as mediators that transform complex protocol behavior into structured, analyzable representations.
Solution Approach 2:
The patent creates simplified copies of TLS protocol implementations in the form of finite-state machine models. Instead of analyzing actual protocol implementations directly, the system learns and replicates their behavioral patterns through state machines. These copied models can be efficiently compared and analyzed to recognize protocol types and versions, reducing the complexity of direct protocol analysis while maintaining recognition precision.
2Measurement precision
If more data packets are sent during fingerprint matching, then recognition accuracy improves, but the process becomes less covert and more detectable
Solution Approach 1:
The patent replaces traditional mechanical fingerprint matching methods that require extensive packet exchange with a model-based approach. By using pre-learned finite-state machine models for comparison, the system can achieve accurate fingerprint recognition with significantly fewer data packets. This substitution reduces the mechanical interaction required between scanner and target, thereby lowering detection risk while maintaining or improving recognition accuracy.
Solution Approach 2:
The patent performs preliminary learning of finite-state machine models during an offline phase, before actual fingerprint matching occurs. This preliminary action prepares the recognition system in advance, allowing it to perform rapid, accurate comparisons with minimal packet exchange during the actual scanning operation. The heavy computational work is done beforehand, enabling covert operation with few packets during the critical matching phase.
3Measurement precision
If manual data marking is performed for protocol analysis, then data accuracy improves, but time consumption and labor costs increase
Solution Approach 1:
The patent implements self-service through automated learning of finite-state machine models from raw protocol data without requiring manual marking or annotation. The system automatically extracts protocol behavior patterns and constructs state machine models through machine learning algorithms. This self-service approach eliminates the need for manual data preparation while maintaining high data accuracy, as the learning process automatically identifies and captures essential protocol characteristics.
Solution Approach 2:
The patent substitutes manual data marking operations with automated machine learning processes. Instead of human experts manually annotating protocol data to create training sets, the system automatically learns from raw traffic data, extracting meaningful patterns and constructing finite-state machine models. This mechanical substitution eliminates time-consuming manual labor while preserving or enhancing data accuracy through algorithmic pattern recognition.
Data Source
AI summary
A method and system for recognizing TLS fingerprints based on finite-state machines is provided, wherein the system at least includes: a model inference module, for learning state machine models of target TLS implementations according to mapping information sent by a message mapping module; a fingerprint extracting module, for analyzing the state machine models and extracting multi-level fingerprints of the target TLS implementations; and a version recognizing module, for verifying the multi-level fingerprints for validity and/or recognizing version information of unknown TLS implementations. As compared to other network protocol identification systems, the present disclosure can identify and judge fine-grained information such as the specific implementation type and version of the specific TLS implementation. At the same time, the inventive method is highly automated, thereby ensuring good usability and scalability.