The rapid growth of digital technologies has led to a significant increase in cyber threats such as malware, ransomware, and other malicious software attacks. Traditional malware detection techniques, which rely on signature-based methods, are often ineffective against new and evolving threats. To address this limitation, this paper proposes a Machine Learning-based Malware Analysis System for efficient detection and classification of malicious software.

The system enables users to upload malware datasets, perform data preprocessing, and train machine learning models to identify patterns associated with malicious activities. By leveraging machine learning algorithms, the system can accurately classify software as benign or malicious based on learned patterns from large datasets. The system also provides data visualization features presenting analysis results through graphs and charts, making it easier to interpret complex data and understand malware behavior trends.

The proposed system enhances the accuracy, speed, and reliability of malware detection compared to traditional approaches. It serves as a scalable and efficient solution for cybersecurity analysis, contributing to improved protection against emerging cyber threats.

Keywords: Malware Detection, Machine Learning, Cybersecurity, Data Preprocessing, Classification, Data Visualization, Zero-Day Attacks.