Malware – Common Attacks & Preventions

In this study, the problem of malware proliferation is examined with emphasis on the role of artificial intelligence (AI) in its formation and propagation. The objective of this research is to analyze common malware attacks, their mechanisms, and prevention strategies, drawing upon literature. Methods involve a qualitative review of reported cases and cybersecurity guidelines published between 2010 and 2024. Findings indicate that AI both exacerbates malware threats through adversarial attacks, automated code generation, and phishing automation and offers tools for improved detection and defense. AI-driven anomaly detection, machine learning based intrusion prevention, and adaptive defense systems show promise in mitigating advanced threats. The review also highlights gaps in governance, adversarial machine learning defenses, and protection for IoT and embedded systems. It concludes that addressing malware proliferation requires coherent frameworks, administrative controls, and AI oversight. Future research should prioritize zero-trust architectures, adversarial machine learning defense strategies, supply chain resilience, and governance policies to ensure sustainable and adaptive cybersecurity defenses.