A Hybrid ML-Driven Framework for File Security: Integrating Static-Dynamic Analysis, Blockchain Storage, and User-Friendly Interfaces
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A Hybrid ML-Driven Framework for File Security: Integrating Static-Dynamic Analysis, Blockchain Storage, and User-Friendly Interfaces
Mithilesh Ramaswamy,
Email: rmith87@gmail.com
Abstract
This paper presents a novel framework for file security that combines machine learning (ML) techniques with static and dynamic analysis, secure blockchain-based storage, and intuitive user interfaces powered by explainable AI (XAI). By integrating static and dynamic analyses, the framework mitigates the limitations of each method in isolation, creating a comprehensive model for detecting and analyzing threats. Blockchain technology provides a tamper-proof, decentralized storage layer for securely sharing analysis results, fostering collaboration among stakeholders. Furthermore, XAI-enhanced interfaces democratize access to advanced security tools by making them transparent and user-friendly. This hybrid approach addresses the growing need for robust, scalable, and accessible solutions in the domain of cybersecurity.
Keywords
File Security, Machine Learning, Static Analysis, Dynamic Analysis, Blockchain, Explainable AI, Usability, Hybrid Framework
DOI: 10.55041/ISJEM0234