Emerging Trends in AI-Powered Malware Detection: A Review of Real-Time and Adversarially Resilient Techniques

Abstract

The rapid evolution of digital threats requires advanced methodologies in malware detection. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Blockchain (BC) have emerged as pivotal technologies in this domain. This review delves into the state-of-the-art trends and techniques in AI powered malware detection systems, mainly focusing on their real-time applications and resilience against adversarial attacks. By in depth analysis diverse algorithms and frameworks, we highlight the significant advantages of AI, including improved detection rates, privacy and the capability to adapt to new malware variants. This study’s findings suggest that while classical signature based detection methods are now defeated by robust and obfuscation techniques, AI powered systems can effectively identify patterns and anomalies by leveraging vast amounts of data. Additionally, this study explores the role of explainable AI in providing transparency and interpretability, which are essential for building user trust and ensuring the reliability of automated decisions. The review consolidates key insights from recent literature, emphasizing innovative approaches that bolster the robustness of detection systems against sophisticated evasion techniques. By mapping the landscape of AI powered malware detection, this study aims to guide future research and promote the development of more resilient cybersecurity solutions capable of withstanding the challenges posed by increasingly sophisticated malware-attacks.