A Deep Learning Framework for Behavioral Ransomware Detection using BILSTM Algorithm

The increasing sophistication of ransomware attacks poses a significant threat to modern computing systems, as traditional signature-based security mechanisms struggle to detect evolving and zero-day variants. This project proposes a deep leaming-based behavioral ransomware detection. framework using the BiLSTM algorithm. The system analyzes dynamic behavioral features such as system call sequences, file access pattems, and process activities to differentiate between benign and malicious behavior. By leveraging BiLSTM's ability to capture sequential dependencies in system activities, the model effectively identifies both known and previously unseen ransomware families. Experimental evaluation demonstrates high detection accuracy, reduced false positive rates, and improved generalization compared to conventional machine learning methods. The proposed framework offers a robust and adaptive solution suitable for real-time ransomware detection in practical cybersecurity environments.