Scalable OS Version Identification Using Security Fingerprints
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Solution Overview
Problem
Existing solutions for determining operating system information, particularly version, are unreliable, require manual maintenance, and lack scalability due to their reliance on preset rules, and do not leverage communications security data effectively.
Innovation Solution
Utilizing machine learning models to analyze sequences from communications security data, such as TLS handshakes, to infer operating system type and version without explicit identifiers, employing hierarchical classifiers and probability-based feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset rules are used to identify operating system information, then the identification process is simple, but the reliability and coverage rate are low
Solution Approach 1:
The patent replaces manual rule-based identification with automated machine learning models that analyze communication security data patterns. These models automatically learn from training data and identify operating system versions without requiring manual rule creation, thereby improving reliability while maintaining operational simplicity through automation.
Solution Approach 2:
The system enables self-service by allowing the machine learning models to automatically train themselves on communication data and continuously improve their identification accuracy. The models autonomously adapt to new operating system versions and patterns without requiring manual intervention for rule updates, enhancing both reliability and coverage.
2Ease of manufacture
If preset rules are used for operating system identification, then implementation is straightforward, but scalability is poor
Solution Approach 1:
The patent creates a universal machine learning framework that can identify multiple operating system types and versions using a single system. The models are trained on diverse communication security data from various devices and operating systems, enabling them to generalize and adapt to new scenarios without requiring separate rule sets for each case, thus achieving both ease of implementation and scalability.
Solution Approach 2:
The system implements dynamic adaptation through machine learning models that continuously learn from new communication data. As new operating system versions are released, the models automatically update their knowledge through continued training on relevant data, allowing the system to scale and adapt to evolving environments without manual rule updates.
3Speed
If communication security data is analyzed using traditional methods, then processing is fast, but measurement precision is insufficient
Solution Approach 1:
The patent segments the communication security data into distinct features and patterns that are relevant for operating system identification. By breaking down the complex handshake data into identifiable sequences and characteristics, the machine learning models can efficiently process the information while extracting precise signals for accurate version identification, achieving both speed and precision.
Data Source
AI summary
A system and method for inferring an operating system version for a device based on communications security data. A method includes identifying a plurality of sequences in communications security data sent by the device; determining an operating system type of an operating system used by the device based on the identified plurality of sequences; applying a version-identifying model to the identified plurality of sequences, wherein the version-identifying model is a machine learning model trained to output a version identifier, wherein the applied version-identifying model is associated with the determined operating system type; and determining the operating system version of the device based on the output of the version-identifying model.