Autonomous Defense Framework for Cryptographic Ransomware Using BiLSTM and Proactive Data Protection
Year: 2025
Type: article
Abstract: : Ransomware has become a dominant and destructive threat in the cybersecurity landscape, targeting organizations across various industries and severely disrupting operations by encrypting data or locking users out of their systems. Victims are often forced to pay a ransom in hopes of restoring access, yet recovery is not always guaranteed. While many ransomware detection tools are available, they frequently fall short when confronted with new, rapidly evolving variants, putting businesses, individuals, and governments at considerable risk.This project introduces an innovative runtime defense mechanism tailored to combat cryptographic ransomware. It utilizes a Bidirectional Long Short-Term Memory (BiLSTM) neural network model capable of detecting and halting ransomware attacks during execution. By observing the time-based behavioral patterns of processes, the BiLSTM system can effectively recognize malicious activity. Its flexibility makes it well-suited to adapt to novel forms of ransomware.In addition to real-time detection, the solution incorporates a forward-looking data protection strategy using Format Preserving Encryption (FPE). This technique obscures sensitive files by altering their extensions to those typically bypassed by ransomware and storing them in concealed directories, thereby reducing the likelihood of compromise.By merging intelligent behavioral analysis with strategic data concealment, this system provides a comprehensive and autonomous shield against both known and emerging ransomware threats. Unlike conventional methods that depend on predefined signatures, this approach offers enhanced security and resilience for mission-critical systems and data.
Authors: Angelin Rosy M, Janani R
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410929217