(untitled) (Cybersecurity and Network Defense Research, 2021)
The rise in global cyber-attacks highlights the need for more sophisticated malware analysis tools and methodologies. As attackers use more advanced techniques, static signatures and heuristic rules are not adequate to detect attacks. The rise of artificial intelligence (AI), including machine learning (ML), deep learning (DL), and anomaly detection, has radically changed the way malware is detected, allowing it to have more adaptive and powerful protection. The paper provides an overview of AI-powered malware analysis — from evolving threats, through basic static and dynamic analysis, to anomaly detection for real-time threat monitoring. As a comparative analysis of AI’s effectiveness at detecting polymorphic, metamorphic and zero-day attacks shows, AI technologies are more effective than traditional signature-based approaches. In addition, issues of adversarial machine learning, model interpretability, and data-based retraining pipelines are discussed, mirroring current debates in industry and academia. It ends by identifying the importance of proactive AI systems in contemporary cybersecurity, and suggests research avenues such as federated learning, explainable AI, and aligning regulatory expectations with cutting-edge security.