AI Powered Intelligent Malware Detection and Threat Analysis System: Design, Implementation, and Performance Evaluation

This paper presented the complete design, implementation, and experimental evaluation of the AI Powered Intelligent Malware Detection and Threat Analysis System—a full-stack cybersecurity platform that combines safe static malware feature extraction, Random Forest classification, automated threat intelligence reporting, and comprehensive scan lifecycle management in a single deployable application. The Random Forest model achieved precision of 95.1%, recall of 93.6%, and F1-score of 94.3% on a balanced 1,500-sample PE executable dataset, outperforming four competing ML algorithms. All nine functional modules passed testing with 100% correctness and sub-450 ms response times. The platform successfully bridges the critical gap between isolated academic malware classifiers and practical, user-accessible cybersecurity tools. Future enhancements include: (i) dynamic sandbox execution in isolated virtual environments for runtime behavioral analysis; (ii) deep learning classifiers (CNN, LSTM, Autoencoder) for malware family identification and zero-day threat generalization; (iii) integration with cloud threat intelligence APIs such as VirusTotal and MISP for real- time global hash verification; (iv) email and SMS alert notifications for critical severity detections; (v) support for additional file types including Android APK, Office macros, and JavaScript payloads; and (vi) multi-tenant enterprise deployment with SOC-level dashboards. These extensions will progressively evolve the platform into a production-grade AI- driven cyber defence ecosystem.

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