Intellectual Property India Publishes Patent Application for System And Method For Evasion-Resistant Malware Analysis Using An In-Sandbox Multi-Agent Consensus Architecture With Dual-Mode Threat Intelligence And A Self-Improving Knowledge Framework

MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641098385 A) filed by Shivakumar Gosula on August 14, 2026, for System And Method For Evasion-Resistant Malware Analysis Using An In-Sandbox Multi-Agent Consensus Architecture With Dual-Mode Threat Intelligence And A Self-Improving Knowledge Framework.

Inventor includes Shivakumar Gosula.

The application for the patent was published on August 21, 2026, under issue no. 34/2026.

Abstract: 062] The present invention relates to a system and method for evasion-resistant malware analysis using an in-sandbox multi-agent consensus architecture with dual- mode threat intelligence guidance and a self-improving knowledge framework. The invention employs an adaptive sandbox orchestration engine that dynamically modifies execution environments to resist malware evasion techniques while multiple specialized intelligent analysis agents independently evaluate static characteristics, runtime behavior, memory activity, network communication, malware evasion indicators, and machine learning predictions. A consensus decision engine combines the independent analytical outputs using weighted confidence mechanisms to generate robust malware classifications. A dual-mode threat intelligence manager selectively utilizes online intelligence services and offline local intelligence repositories to maintain continuous analytical capability. A self-improving knowledge repository continuously stores behavioral intelligence, consensus outcomes, analyst feedback, and machine learning features to enhance future malware detection performance. The invention further provides explainable artificial intelligence reporting, automated incident response, and adaptive learning mechanisms, thereby improving malware detection accuracy, reducing false classifications, resisting sophisticated sandbox evasion strategies, and strengthening enterprise cybersecurity against both known and previously unseen malware threats. Accompanied Drawing [FIGS. 1-2]

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