AI-POWERED MALWARE DETECTION

Abstract

The fast development of digital technology has caused more advanced cyber dangers, especially malware attacks that take advantage of system weaknesses. Old ways of finding malware, like using signatures or heuristics, often miss new or changing threats. To fix this, AI-based malware detection systems have become a good solution. These systems use machine learning and deep learning to study big sets of data, find hidden patterns, and spot unknown malware with high accuracy. This study looks at how to design and build AIbased malware detection methods that mix static and dynamic analysis to improve performance. The method speeds up detection, cuts down on false alarms, and has the ability to learn and adapt to new threats quickly. The results show that AI models work much better than old methods, making them a key part of today's cybersecurity systems.