NEUROMALWARE: LEVERAGING DEEP NEURAL NETWORKS FOR INTELLIGENT THREAT ANALYSIS

Abstract

Malware attacks have become increasingly sophisticated, posing significant threats to cybersecurity. Traditional signature-based detection systems often fail to identify new and polymorphic malware variants. This paper presents NeuroMalware, a deep learning-based intelligent malware detection framework designed to identify both known and novel malware threats. Leveraging deep neural networks (DNNs), the proposed system automatically extracts complex features from binary files and system behaviors, enhancing detection accuracy. Experimental evaluations demonstrate that NeuroMalware outperforms conventional machine learning and signature-based approaches in terms of detection rate, robustness, and adaptability to unseen malware