TLS Fingerprint Analysis for Operating System Detection
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Solution Overview
Problem
Current technologies fail to accurately detect and differentiate between declared and actual operating systems and agents used in network communications, which can lead to fraudulent activities due to the falsification of user agent strings, compromising security and fraud detection.
Innovation Solution
A system and method that utilize TLS fingerprints to create a model correlating these fingerprints with actual operating systems and agents, employing machine learning techniques like the Naïve Bayes algorithm to predict the correct operating system and agent, and flag potential fraudulent activities by analyzing the correlation of TLS fingerprints with known 'bad' transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user agent strings are used to identify operating systems and agents, then the detection process is simple and fast, but the accuracy is low due to falsification
Solution Approach 1:
The patent introduces TLS fingerprints as an intermediary mechanism between the client and server for identification. Instead of directly trusting user agent strings, the system uses TLS fingerprints (derived from client hello packets) as a mediator to verify the actual operating system and agent, thereby resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent replaces the mechanical/string-based user agent string verification with a cryptographic approach using TLS fingerprints. The fingerprinting mechanism substitutes the unreliable text-based identification with a more robust cryptographic verification method that analyzes the client hello packet structure.
2Measurement precision
If TLS fingerprints are captured and analyzed, then the accuracy of detecting actual operating systems and agents improves, but the complexity of the detection system increases
Solution Approach 1:
The system performs preliminary actions by capturing and storing TLS fingerprints during the initial client hello phase of the TLS handshake. This preliminary fingerprinting allows the system to have identification data ready before actual authentication or detection is needed, reducing the complexity of real-time analysis.
Solution Approach 2:
The patent creates a copy of the client hello packet information in the form of a TLS fingerprint. Instead of analyzing the entire complex packet structure during detection, the system uses this pre-extracted fingerprint copy, which simplifies the detection process while maintaining high accuracy.
3Measurement precision
If machine learning models are trained on TLS fingerprints, then the prediction accuracy of agents and operating systems improves, but the computational resources and time required increase
Solution Approach 1:
The machine learning model is trained in advance on a dataset of TLS fingerprints with known operating systems and agents. This preliminary training phase allows the model to learn patterns and correlations beforehand, so that during actual operation, the system can perform fast inference without requiring real-time training or complex computations.
4Reliability
If TLS fingerprints are used for fraud detection, then the ability to identify fraudulent activities improves, but the system complexity and processing overhead increase
Solution Approach 1:
The patent uses TLS fingerprints as an intermediary for fraud detection by comparing them against a database of known fraudulent patterns and blacklisted fingerprints. This intermediary approach allows the system to detect fraud without requiring complex real-time analysis of entire communication sessions, thereby improving reliability while managing system complexity.
Data Source
AI summary
A computer system receives a first information detailing a TLS fingerprint. A computer system determines an amount of bad transactions associated with the TLS fingerprint, wherein a bad transaction is a transaction involved in one or more fraudulent activities. The computer system determines whether the amount of bad transactions associated with the TLS fingerprint exceeds a threshold amount.