Deep Learning Framework for Behavioral Ransomware Detection using BiLSTM

Ransomware attacks have become one of the most dangerous cybersecurity threats affecting organizations and individuals worldwide. Traditional signature-based detection systems fail to identify newly emerging ransomware variants due to their rapidly evolving behavior. This project proposes a Deep Learning Framework for Behavioral Ransomware Detection using Bidirectional Long Short-Term Memory (BiLSTM) networks. The system analyzes API call sequences, file access patterns, encryption activities, and process behavior in real time to detect malicious activities before complete system compromise occurs. The proposed model uses behavioral analysis instead of static signatures, enabling early and accurate detection of zero-day ransomware attacks. Data preprocessing techniques such as feature extraction, normalization, and SMOTE balancing are applied to improve classification performance. Experimental results demonstrate improved accuracy, reduced false positives, and faster detection compared with traditional machine learning approaches. This framework can be integrated into enterprise security systems to provide intelligent, scalable, and real-time ransomware protection.