Malware Analysis

The increasing prevalence and sophistication of malware attacks have made it essential to develop advanced, automated detection systems. Traditional signature-based methods often fall short, particularly when faced with novel or polymorphic threats. This paper introduces an intelligent malware analysis framework that leverages deep learning models for effective and efficient real-time detection. Integrating both static and dynamic analysis, the system applies deep neural networks to classify software as malicious or benign. The framework aims to enhance detection accuracy, minimize false positives, and improve overall user experience.