Host OS Detection via Transport Layer Probes and Machine Learning
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Solution Overview
Problem
Determining a host operating system remotely is challenging due to changing NAT configurations, intermediate filtering devices, packet normalization, and system hardening, especially without credentialed access, and traditional methods are manual, error-prone, and can disturb the host/network.
Innovation Solution
A method using transport layer probe metadata and machine learning to detect operating systems by training a model with response patterns from TCP probes, allowing for accurate OS prediction without intrusive testing or credentialed access, using a probe sequence comprising TCP probes to gather response metadata for training and detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional active fingerprinting methods are used to identify operating systems, then OS detection capability is improved, but host/network disturbance increases
Solution Approach 1:
The patent uses transport layer probe metadata as an intermediary to indirectly identify operating systems without directly interrogating application-layer services. By analyzing TCP header options, window scales, and other transport layer characteristics, the system can infer OS type without crafting specific application-level requests that would disturb the host.
Solution Approach 2:
The patent replaces manual, mechanical fingerprinting methods with automated machine learning-based detection. The trained OS detection model automatically analyzes transport layer metadata patterns to identify operating systems, eliminating the need for manual crafting of probe sequences and reducing human error while maintaining detection accuracy.
2Measurement precision
If application-level protocol metadata is used for OS identification, then detection capability is improved, but reliability decreases when hosts are hardened
Solution Approach 1:
Instead of relying on application-level protocol metadata that may be filtered or normalized by hardened hosts, the patent inverts the approach by using transport layer metadata from TCP probes. These lower-level characteristics are harder to filter or normalize without breaking fundamental TCP functionality, making detection more reliable against hardened systems.
Solution Approach 2:
The patent segments the OS identification process into distinct transport layer characteristics (TCP window scale, TCP header options, MSS values, etc.) that can be independently analyzed. This segmentation allows the machine learning model to evaluate multiple separate features rather than relying on a single application-level metadata source, improving robustness against filtering.
3Measurement precision
If manual OS lookup maintenance is performed, then initial detection accuracy is achieved, but productivity decreases and errors increase over time
Solution Approach 1:
The system implements self-service through automated machine learning model training and updating. The OS detection model is trained on datasets that automatically capture emerging operating system characteristics, eliminating the need for manual updates to lookup tables. The system adapts to new OS versions and variants automatically through retraining on fresh data.
Solution Approach 2:
The patent transitions from static OS lookup tables to dynamic machine learning models with adjustable parameters. The model's decision boundaries and feature weights can be optimized through training, allowing the system to adapt to changing OS landscapes without manual intervention. This parameter-based approach replaces rigid manual maintenance with flexible automated optimization.
Data Source
AI summary
Techniques, methods and/or apparatuses are disclosed that enable detection of an operating system of a host. Through the disclosed techniques, an operating system detection model, which may be a form of a machine learning model, may be trained to detect operating system. The operating system detection model may be provided to an operating system detector to detect operating system of a host utilizing transport layer probes without the need to have credentialed access to the host.