AI-Powered Advanced Threat Protection: A Novel Framework for Next-Generation Malware Defense

As malware threats evolve in complexity and scale, traditional detection and mitigation strategies face increasing limitations. The integration of Artificial Intelligence (AI) into advanced threat protection (ATP) frameworks offers a transformative approach to combating sophisticated malware attacks. This paper introduces a novel AI-powered framework that leverages machine learning (ML), deep learning (DL), and graph-based algorithms for next-generation malware defense. The proposed system combines real-time threat intelligence, predictive anomaly detection, and adaptive remediation strategies to protect systems against known and emerging threats. By synthesizing insights from recent academic research, this framework provides a comprehensive model that addresses challenges such as obfuscated malware, polymorphic attacks, and zero-day vulnerabilities. This paper also highlights the importance of AI’s explainability, continuous learning, and collaboration with traditional ATP systems, paving the way for a robust and scalable malware defense solution.