International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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

Author(s) Mithilesh Ramaswamy
Country USA
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.
Keywords Artificial Intelligence, Advanced Threat Protection, Malware Detection, Machine Learning, Deep Learning, Anomaly Detection, Zero-Day Attacks, Adaptive Remediation
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-23
Cite This AI-Powered Advanced Threat Protection: A Novel Framework for Next-Generation Malware Defense - Mithilesh Ramaswamy - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.22481
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.22481
Short DOI https://doi.org/g82hrm
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