Published Paper Details:

The threat of ransomware is considerable in cybersecurity risk and often goes undetected by traditional signature-based detection approaches. In this paper, we present a deep learning-based behavioral analysis framework supporting pro-active detection and disruption of ransomware. Rather than depending on signatures, the framework analyzes system-level activities, such as file encryption, abnormal access, and process relations. The framework utilizes Long Short- Term Memory (LSTM) networks to analyze temporal activities and Recurrent Neural Networks (RNNs) to extract features, enabling real-time identification of ransomware. Our system detects anomalies present in suspicious behavioral patterns, it provides warnings to the administrators, and automatically either quarantines files or isolates from the network. By using deep learning, our framework detects better and has fewer false positives compared to traditional methods. This study demonstrates the potential for deep learning for analyzing behavior for ransomware protection purposes, giving us a strong and adaptive means of defending against evolving cybersecurity threats.