OS Inference via TCP Fingerprinting ML Models
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Solution Overview
Problem
Existing solutions for determining device operating systems in cybersecurity face challenges due to the lack of explicit identification and reliance on manual, rules-based mechanisms that are cumbersome and less reliable, especially with evolving operating systems.
Innovation Solution
A method and system utilizing machine learning models, specifically sequence-based and distribution dissimilarity models, to infer operating systems from TCP fingerprinting data, including applying a sequence-based model to TCP options and a distribution dissimilarity model to metadata field distributions, with an operating system inference model trained on these features to provide accurate inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual rules-based mechanisms are used to determine operating systems, then the process is simple to implement, but the accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces manual rules-based mechanisms with machine learning models that automatically analyze TCP packet patterns. The sequence-based model and distribution dissimilarity model process TCP options sequences and metadata field distributions to infer operating systems, substituting human-defined rules with automated statistical learning approaches that adapt to evolving OS behaviors.
Solution Approach 2:
The patent transforms operating system identification from a rule-based classification problem into a statistical pattern recognition problem. By changing the approach from fixed rules to dynamic parameter analysis (TCP option sequences, metadata distributions), the system adapts to new operating system variations without requiring manual rule updates.
2Reliability
If explicit operating system identification is required from devices, then the information is directly available, but the lack of industry standards makes this unreliable
Solution Approach 1:
The patent uses TCP packet metadata and options sequences as an intermediary to indirectly identify operating systems. Instead of relying on devices to explicitly report their OS (which requires industry standards), the system analyzes intermediate TCP communication patterns that naturally reveal operating system characteristics without requiring direct device cooperation or standardization.
3Adaptability or versatility
If traditional profiling methods are used, then the implementation is straightforward, but they cannot keep up with evolving operating systems
Solution Approach 1:
The patent performs preliminary training of sequence-based and distribution dissimilarity models using historical TCP packet data from multiple operating systems. This preliminary action creates pre-trained models that can quickly adapt to new operating systems by learning from patterns in the training data, reducing the time required for future adaptations compared to creating rules from scratch.
Data Source
AI summary
A system and method for inferring device operating systems. A method includes applying a sequence-based model to an option-types sequence in order to output a plurality of first features, wherein each of the first features is a value representing a probability that the options-type sequence is associated with a respective operating system; applying a distribution dissimilarity model to metadata field distribution data extracted from the headers of the packets sent by the device in order to output a plurality of second features, wherein the plurality of second features includes a plurality of distances, wherein each distance is based on a difference between a distribution of values of each metadata field indicated in the metadata field distribution data; and applying an operating system inference model to the plurality of first features and the plurality of second features in order to output an inferred operating system for the device.