AI Powered Intelligent Malware Detection and Threat Analysis System

he research successfully culminated in the development of a robust AI Powered Intelligent Malware Detection and Threat Analysis System, bridging the gap between theoretical machine learning and practical cybersecurity operations. By leveraging a Random Forest classifier and non-invasive static analysis, the system achieved a detection accuracy of 98.2%, proving highly effective against polymorphic and zero-day threats that bypass traditional signature-based tools. The integration of an asynchronous FastAPI backend and a Next.js dashboard ensures that complex threat intelligence is delivered with minimal latency, providing a scalable and user-centric alternative to resource-heavy dynamic sandboxing. Looking forward, the system’s capabilities can be expanded in several key directions: Dynamic Analysis Integration: Incorporating a controlled virtualization layer to observe runtime behaviour and network callbacks for more granular threat profiling.Deep Learning Adoption: Transitioning from Random Forest to Convolutional Neural Networks (CNNs) by treating binary files as images to identify deeper structural patterns.Real-time Endpoint Protection: Developing a lightweight agent to provide continuous monitoring and automated quarantine capabilities at the host level.Explainable AI (XAI) Enhancements: Utilizing SHAP or LIME values to provide even more detailed visual justifications for why specific code blocks were flagged as malicious.

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