Artificial Intelligence for Malware Analysis
Amit Kumar Tyagi, Santosh Reddy Addula · 2024
Due to the rapid increase of malware threats, the cybersecurity environment is encountering a major obstacle. In order to effectively battle these constantly changing malicious entities, the field of malware analysis has embraced AI as a game-changing force. This chapter offers a comprehensive overview of the current methods, resources, and trends in malware analysis. A comprehensive examination of AI methods used to malware analysis is also part of this chapter. At the outset of our examination, we define malware, demonstrate the variety of malware attacks, and stress the critical need for improved analysis tools. After that, we'll talk about how AI-powered approaches have changed malware analysis forever. Several of these techniques fall under this category, including hybrid approaches, static analysis, and dynamic analysis. The report presents a detailed examination of the several deep learning and machine learning algorithms used for malware detection and classification, including their benefits, drawbacks, and real-world effects. We also take a look at how AI-powered anomaly detection and behavioral analysis have uncovered hitherto undiscovered malware variants. In addition to its role in malware detection, artificial intelligence has also proven crucial in making systems more resilient to malware attacks.