Malware Sample Selection via Clustering for Detection

The patent segments malware analysis into multiple independent sandbox environments with different configurations and resource profiles. By dividing the analysis process across multiple sandboxes rather than relying on a single conventional sandbox, the system prevents malware from evading detection through sandbox awareness, as each sandbox presents a unique environment that malware cannot predict or adapt to uniformly.

The patent introduces a new dimension to malware detection by analyzing malware behavior across multiple sandbox environments simultaneously. Instead of relying on single-environment detection, the system evaluates malware performance across diverse sandbox configurations, adding environmental variability as an additional detection dimension that undermines malware's ability to evade through sandbox detection techniques.

The patent extracts and analyzes only the most critical and representative malware samples for comprehensive intelligence gathering. By identifying and focusing analysis on high-value samples that exhibit distinctive or dangerous behaviors, the system obtains sufficient malware intelligence without the need to perform exhaustive analysis on every sample, thereby reducing computational resource consumption while maintaining effective threat detection capability.

The patent applies partial action by performing comprehensive analysis on a selective subset of malware samples rather than all samples. This approach provides adequate malware intelligence for detection purposes while avoiding the excessive computational resource consumption that would result from analyzing every sample in full detail, achieving an optimal balance between intelligence gathering and resource usage.