Cyber Security AI for Ransomware Detection

Abstract

The rapid increase in ransomware attacks has created significant challenges for data security and system reliability in modern computing environments. Traditional security solutions often fail to detect sophisticated ransomware due to their dynamic and evolving behaviour. To overcome this limitation, this project proposes an intelligent ransomware detection and prevention system using Artificial Intelligence techniques. The system continuously monitors file activities and analyzes behavioural patterns to identify suspicious actions in real time. It employs risk scoring and adaptive learning mechanisms to improve detection accuracy and respond quickly to potential threats. When malicious activity is detected, the system automatically quarantines affected files and generates alerts to prevent further damage. The proposed system enhances cybersecurity by providing early detection, minimizing data loss, and strengthening protection against ransomware attacks.

Introduction

It explains that traditional antivirus methods, which rely mainly on signature-based detection, are often ineffective against new and evolving ransomware. To overcome this, the proposed system uses artificial intelligence and behavioral analysis to continuously monitor system activities such as file access, modification, and encryption patterns. It assigns risk scores to detect suspicious behavior in real time and can automatically quarantine infected files, generate alerts, and adapt over time through learning mechanisms.

Related research highlights the use of machine learning and deep learning models (such as Random Forest, SVM, CNN, and LSTM) for ransomware detection, but notes limitations like high computational cost, false positives, and poor adaptability. Existing systems also often lack integrated real-time monitoring, adaptive learning, and intelligent response features.

The proposed methodology includes data collection from system logs, preprocessing to clean and normalize data, behavioral analysis to detect anomalies, risk scoring for decision-making, file quarantine for containment, and adaptive learning to improve performance over time.

Conclusion

The increasing frequency and complexity of ransomware attacks have created significant challenges for modern cybersecurity systems. Traditional security solutions often struggle to detect advanced and evolving ransomware threats, leading to data loss and system disruption. To address these challenges, this project developed an intelligent ransomware detection and prevention system using Artificial Intelligence techniques. The proposed system continuously monitors file system activities, analyzes behavioural patterns, and assigns risk scores to detect suspicious actions in real time. By integrating automated quarantine mechanisms and alert generation features, the system effectively prevents the spread of ransomware and minimizes potential damage. The implementation of adaptive learning techniques further enhances detection accuracy by enabling the system to adapt to new and emerging threat patterns. Overall, the developed system provides a reliable, efficient, and proactive cybersecurity solution for protecting digital systems against ransomware attacks. The results demonstrate improved threat detection capability, reduced response time, and enhanced system security. This project contributes to the advancement of intelligent cybersecurity technologies and provides a strong foundation for future research and development in ransomware detection and prevention systems.

References

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Copyright

Copyright © 2026 Mrs. M Indumathi , Madhumitha S, Poongodi P, Saranya V, Thenmathi S. 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.