AI-Powered Advanced Threat Protection: A Novel Framework for Next-Generation Malware Defense
Year: 2024
Type: article
Abstract: 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.
Author: Mithilesh Ramaswamy -
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4406616007