A ROBUST DEEP LEARNING FRAMEWORK FOR MALWARE DETECTION AND CLASSIFICATION

Abstract

The rapid growth of digital technologies and internet connectivity has led to a significant increase in malware attacks targeting computer systems and networks. Traditional signaturebased malware detection techniques are no longer sufficient to handle sophisticated and evolving malware variants. Deep learning has emerged as a powerful approach for automatic feature extraction and accurate malware detection. This paper presents a robust deep learning framework for malware detection and classification that effectively identifies malicious software with high accuracy. The proposed framework leverages deep neural network architectures to analyze malware behavior and patterns. It enhances detection capability while reducing false positives. Experimental results demonstrate that the proposed approach outperforms conventional machine learning methods. The framework is scalable and adaptable to emerging malware threats.