Firmware Compromise Detection via Statistical Analysis
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Solution Overview
Problem
Current antivirus and malware detection techniques are unable to effectively detect compromises in firmware due to their reliance on signature-based methods, which are inadequate for dynamic firmware components that vary at each boot, leaving them susceptible to undetected compromises.
Innovation Solution
Implementing a method to analyze firmware using statistics-based techniques by accessing and storing firmware in memory regions during pre-boot, then making it accessible at runtime as files within a file system, allowing antivirus solutions to perform statistical analyses for detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection methods are used for firmware, then detection simplicity is maintained, but detection capability is insufficient for dynamic firmware components
Solution Approach 1:
The patent changes the detection parameter from static signatures to dynamic statistical features. Instead of comparing fixed hash values, the system collects multiple firmware samples and analyzes statistical parameters such as byte frequency distributions, entropy values, and n-gram patterns. This allows detection of compromises in dynamic firmware components while maintaining automated operation.
Solution Approach 2:
The patent creates copies of firmware images from multiple boot instances and stores them in a collection for analysis. By copying and comparing firmware from different boots, the system can identify anomalies that deviate from the expected variation pattern, enabling detection without requiring the firmware to remain static.
2Measurement precision
If firmware is analyzed using statistics-based techniques, then detection accuracy improves, but resource requirements increase
Solution Approach 1:
The patent applies partial action by analyzing only specific statistical features of firmware rather than performing exhaustive full-firmware comparison. It focuses on key indicators such as byte frequency distributions and entropy calculations on critical sections, achieving effective detection with reduced computational overhead compared to complete firmware analysis.
Solution Approach 2:
The patent performs preliminary actions by collecting firmware samples from multiple boots in advance and pre-processing them into statistical feature sets. This preprocessing step converts raw firmware binary data into compact statistical representations, reducing the computational burden during actual detection operations.
3Reliability
If firmware samples from multiple boots are collected and compared, then detection reliability improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential statistical features from complete firmware images, separating the critical detection-relevant information from the full binary data. By extracting features such as byte frequency counts, entropy values, and condensed n-gram patterns, the system reduces data processing complexity while maintaining detection reliability.
Data Source
AI summary
Statistical detection of firmware-level compromises can be enabled and performed on a computing system. During pre-boot, a pre-boot agent can access firmware loaded in memory and cause it to be stored in a manner that will allow the firmware to be accessed at runtime. During runtime, the firmware can be accessed and stored as files in the file system or other storage location accessible to an antivirus solution. The antivirus solution can then analyze the files using statistics-based techniques to thereby detect compromises in firmware.