A Deep learning framework for early detection of Ransomware communication patterns in encrypted network using transfer learning Case study: Financial Enterprise Networking
This paper presents an LSTM-based deep learning framework that utilizes transfer learning on encrypted network flow features to detect ransomware without payload decryption, demonstrating the critical necessity of 12–18 month retraining cycles to counteract temporal model drift while achieving high detection accuracy through a confidence-based ensemble system integrated with Wazuh SIEM.