Binary Comparison for Detecting Unintended Software Functionality

The patent segments the software analysis into multiple independent components: SBOM generation, VEX data collection, behavioral feature extraction, and risk assessment. This segmentation allows each component to specialize in specific aspects, with behavioral analysis separately examining function calls, system interactions, and execution patterns to detect unintended functionality without compromising the vulnerability assessment pipeline.

The patent adds a temporal dimension by analyzing behavioral changes between software versions. Instead of static vulnerability listing, the system compares behavioral features across versions to identify new unintended functions. It also adds a functional dimension by extracting and analyzing specific behavior patterns (function calls, system interactions) to determine live impact, transforming the assessment from component-level to behavior-level analysis.

The patent implements feedback mechanisms where behavioral analysis results feed back into the risk assessment process. The system continuously monitors behavioral features, compares them against baseline expectations, and adjusts vulnerability assessments accordingly. This feedback loop reduces false positives by validating vulnerability claims against actual observed behavior, allowing cyber analysts to focus only on confirmed high-risk issues.

The patent creates virtual copies of software behavior through dynamic instrumentation and sandboxing. Instead of directly analyzing potentially malicious code execution, the system copies and monitors behavior patterns in isolated environments. This copying approach enables comprehensive behavioral analysis without exposing analysts to actual malware, reducing both false positives and time required for manual verification.

The patent merges SBOM component listing with behavioral feature extraction and risk assessment into a unified analysis platform. The system combines static component information from SBOMs with dynamic behavioral data, function call graphs, and execution patterns to create a comprehensive view of software risk. This merging allows the system to detect malicious functionality by analyzing how components actually behave rather than relying solely on declared compositions.

The patent changes the analysis parameters from static component identifiers to dynamic behavioral metrics. Instead of assessing risk based solely on component lists and vulnerability databases, the system monitors parameters such as function call frequencies, system resource consumption, network interactions, and execution timing patterns. These parameter changes enable the detection of malicious functionality that may not be captured in traditional SBOM data.

The patent implements self-service automation where the system performs its own analysis without requiring manual intervention. Automated behavioral feature extraction, machine learning-based risk assessment, and comparative analysis across versions are all performed automatically. The system self-calibrates using baseline data from known-good software versions and continuously improves its detection accuracy through automated learning, eliminating the need for manual analysis while maintaining high precision.

The patent replaces manual mechanical analysis with automated computational systems. Instead of human analysts manually reviewing code and behavior, the system uses automated tools for dynamic instrumentation, sandboxing, machine learning classification, and statistical analysis. This substitution maintains detection accuracy while dramatically reducing complexity from the perspective of the analyst, as the automated system handles all analytical tasks.