Autonomous Defense Framework for Cryptographic Ransomware Using BiLSTM and Proactive Data Protection

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.

Introduction

Ransomware poses a growing cybersecurity threat requiring proactive defense strategies. The proposed solution integrates two advanced technologies: RanGAN, a generative adversarial network designed to simulate and detect ransomware variants in real time, and Hash Conceal, a cryptographic method that protects critical files by hiding them behind secure hash-based references.

The system includes several modules:

• RanFooler Web Tool: A multi-layer web infrastructure with a load balancer that protects users from ransomware by analyzing attacks and reporting compromised files.

• End User Configuration: Admins train the RanGAN model using ransomware datasets and deploy it for live monitoring, while users register their devices and select files to protect.

• Ransomware Classification and Training: Uses Byte files and machine learning models (BiLSTM and GRU) to learn ransomware patterns for accurate detection.

• Attacker Model: Describes ransomware delivery through obfuscated payloads embedded in compromised web pages.

• Ransomware Prediction and Prevention: RanFooler offers antivirus and preventive scans, real-time forecasting using RanGAN’s generated ransomware data, and dynamic blocking/removal of threats.

• Hash Concealer: Secures files by storing them in hidden layers and using linked reference files to allow safe access without exposing originals.

Results showed the system effectively detects ransomware with high accuracy and low false positives, anticipates threats in real time, and safeguards critical files while operating efficiently with minimal resource impact.

Conclusion

This autonomous security framework delivers a smart and forward-looking solution to address the growing threat of cryptographic ransomware. Utilizing BiLSTM neural models, the system learns and identifies the sequential behavior of ransomware, allowing it to recognize both known and novel attacks with high precision. The use of synthetic ransomware instances, generated through RanGAN, further improves the system’s adaptability to new and evolving threats in real time. In addition, the integration of Hash Conceal methods and a Linker-driven file access mechanism adds a strong layer of data protection. By concealing vital files in a hidden environment while still allowing user interaction via linked access points, the system effectively prevents unauthorized encryption. The framework’s built-in real-time monitoring and automatic notifications enhance its ability to respond promptly to any suspicious activity. Overall, the framework offers a reliable and adaptive defense against ransomware, combining accurate threat detection with proactive data safeguarding to protect systems in an increasingly hostile cybersecurity landscape.

References

[1] J. Choi, J. Lee, G. Lee, J. Yu, and A. Park introduced a method for protecting files from malicious attacks by concealing them within hidden directories. Their work, published in the Journal of the Korea Society of Industrial Information Systems (Vol. 27, No. 2, 2022), details a strategy for safeguarding files from unauthorized modifications or encryption. [DOI: 10.9723/jksiis.2022.27.2.001] [2] J. Yuste and S. Pastrana provided a comprehensive investigation into the Avaddon ransomware, exploring its behavior, impact, and potential methods for decrypting infected systems. This research appeared in Computers & Security (Vol. 109, October 2021) under article number 102388. [DOI: 10.1016/j.cose.2021.102388] [3] S. Homayoun et al. proposed a pattern-mining approach to identify ransomware threats by recognizing abnormal system behavior. Their study, titled “Know abnormal, find evil”, was published in IEEE Transactions on Emerging Topics in Computing (Vol. 8, No. 2, April 2020, pp. 341–351). [4] K. Lee, S. Lee, and K. Yim explored a machine learning-based method to detect ransomware by analyzing file entropy in backup environments. Their findings were shared in IEEE Access (Vol. 7, 2019, pp. 110205–110215), emphasizing the importance of backup integrity in ransomware resilience. [5] B. Zhou and colleagues examined the potential of using hardware performance counters for malware detection, questioning whether this technique is a reliable indicator or just a misconception. This research was presented at the Asia Conference on Computer and Communications Security in May 2018 (pp. 457–468).

Copyright

Copyright © 2025 Angelin Rosy M, Janani R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.