Blockchain-Enabled Security Framework against Ransomware Attacks Using Machine Learning

Abstract

In this presentable paper, we first describe and analyze the background of a certain discipline by reviewing the recent scientific literature on this subject and summarizing it about issues of the new research. Ransomware through reasoning can also be considered the last significant stage for cyber extortion blocking access to the resources of the target organization until submission by the latter to certain coercion or payment. There is a separate class of malware known as ransomware. When a computer or some other device suffers a ransomware malware attack, such device is either locked/held hostage or the data within the device is encrypted. Ransom demands (usually a small sum) are placed on the victims for providing the data order for the data translation in the form of a decryption key. Due to these attacks, surveillance means and protective programs are recommended given the prevention of ransomware epidemic outbreaks. The most vulnerable targets are probably those classed as organizations, such as financial institutes and healthcare sectors. Blockchain technology prevents tempering which makes it much more effective than the traditional centralized approach of data storage. Such aspects of blockchain technology can enhance the security perimeter for the detection and prevention of ransomware attacks even more. The objectives of the research are to demonstrate the extent of the problem and to show how problems are identifiable within datasets, which is through the application of machine learning. In this paper, we propose a new security framework that applies machine learning to prevent ransomware attacks and is based on the principles of blockchain technology.

Introduction

Overview:
Ransomware is a fast-growing cybersecurity threat that encrypts or deletes important data and demands ransom. It affects critical sectors like healthcare, banking, and government, causing severe operational and financial damage. Traditional security approaches are reactive and inadequate. A more proactive solution is needed.

Proposed Solution:
The paper proposes a Blockchain-Enabled Security Framework combining blockchain and machine learning (ML) to proactively detect, contain, and recover from ransomware attacks, with a focus on smart healthcare systems.

Framework Components:

1. Continuous Monitoring: Real-time monitoring of systems to detect and remove suspicious files, preventing the spread of ransomware.

2. Ransomware Detection: ML models analyze Portable Executable (PE) files using feature extraction and classification to detect infections.

3. Blockchain-Based Backup & Recovery: Decentralized data storage via blockchain ensures tamper-proof and retrievable backups, eliminating the need to pay ransom.

4. Secure Ransom Payments: If required, ransom payments are handled through blockchain to ensure secure and traceable transactions.

Existing Systems Limitations:
Current methods are mostly reactive (post-attack) and rely on signature-based detection, which fails against sophisticated ransomware. Centralized storage also presents a single point of failure.

System Architecture:

• User Module: Allows users to scan files and manage backups.

• Attacker Module: Demonstrates ransomware behavior for testing.

• Detection Module: Uses ML algorithms (KNN, SVM, Random Forest) to classify files.

• Blockchain Backup Module: Stores backups on decentralized networks using IPFS with content identifiers (CIDs).

• Payment Module: Manages secure ransom payments via blockchain.

Algorithm Development:

• Data Collection: Builds datasets of legitimate and infected PE files.

• Feature Selection: Uses variance threshold and correlation analysis to refine features.

• Classification: Employs Lazy Classifier to select the best ML model for detection.

• Blockchain Integration: Secure backup and transaction protocols using smart contracts.

Implementation:

• Combines React-based UI, ML-based file scanning, decentralized storage, and blockchain-backed recovery.

• Real-time detection and quarantine of infected files.

• Automated backup and recovery managed by smart contracts.

Testing:

• Functional, security, and performance tests ensure the system's resilience and efficiency.

Future Enhancements:

• Add decryption tools for Crypto ransomware.

• Explore advanced ML models to improve detection.

• Strengthen recovery procedures to handle evolving threats.

Conclusion

The Blockchain-Based Security Framework to Ransomware Using Machine Learning, a system capable of responding to ransomware attacks through the project presented here integrates the concepts of machine learning and blockchain in addressing the challenge of ransomware in a preventative measure and a remedial measure to an extent. Powered by a React user interface, the system leverages sophisticated feature extraction, variance thresholding, and correlation to scan Portable Executable (PE) files to provide appropriate data for classification. To maximize the performance capabilities of the framework in separating good and bad files, a Lazy classifier algorithm is employed to determine the best machine learning model suitable for the task. Moreover, IPFS is used to provide data and files in a distributed manner, while distributed ledger system of blockchain is used to track Content Identifiers (CIDs) enabling assurance of safety, accessibility and backup of data eliminating the challenges that come with the hazards of centralized storage. The system also implements an automatic response mechanism along with a continuous monitoring system that is able to contain suspicious acts and curb the spread of an infection. The blockchain-enabled security framework against ransomware attacks , in contrast to other existing systems, is a developed system that is decentralised and aimed at responding to attacks before they take place, with specific regard to the exceptional preparedness cut-out especially in health care. Enhancements in the fierceness of the warfare would most likely incorporate some aspects of deep learning and more changeable parameters to improve efficiency and responsiveness to new forms of ransomware. Taken all together, blockchain-enabled security framework against ransomware attacks represents a solid structure wherein machine learning and blockchain technologies are interconnected to provide a viable and dependable solution to ransomware.

References

[1] S. S. Chakkaravarthy, D. Sangeetha, M. V. Cruz, V. Vaidehi, and B. Raman, “Design of intrusion detection honeypot using social leop ard algorithm to detect IoT ransomware attacks,” IEEE Access, vol. 8, pp. 169944–169956, 2020. [2] M. Wazid, A. K. Das, V. Odelu, N. Kumar, and W. Susilo, “Secure remote user authenticated key establishment protocol for smart home environment,” IEEE Trans. Dependable Secure Comput., vol. 17, no. 2, pp. 391–406, Mar./Apr. 2020. [3] S. Tian, W. Yang, J. M. L. Grange, P. Wang, W. Huang, and Z. Ye, “Smart healthcare: Making medical care more intelligent,” Global Health J., vol. 3, no. 3, pp. 62–65, 2019. [4] E. Berrueta, D. Morato, E. Magana, and M. Izal, “A survey on detec tion techniques for cryptographic ransomware,” IEEE Access, vol. 7, pp. 144925–144944, 2019. [5] D. Farhat and M. S. Awan, “A brief survey on ransomware with the perspective of Internet security threat reports,” in Proc. 9th Int. Symp. Digit. Forensics Security (ISDFS), Elazig, Turkey, 2021, pp. 1–6.

Copyright

Copyright © 2025 Prince Yadav, Harsh Patel, Meraj Shaikh, Arpit Dahat, Dr. Vinod Wadne . 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.