Ransomware attacks have emerged as one of the most devastating cyber threats facing organizations worldwide, with global damages exceeding $20 billion annually and attack frequencies increasing by over 150% in recent years. Traditional signature-based detection methods have proven insufficient against the evolving sophistication of ransomware variants, which now employ advanced encryption algorithms, polymorphic code, and fileless attack techniques. This research paper investigates the application of machine learning (ML) and artificial intelligence (AI) techniques for ransomware detection and prevention in enterprise network environments. The study examines various ML approaches including supervised learning algorithms such as Random Forest, Support Vector Machines, and Deep Neural Networks, as well as unsupervised methods like clustering and anomaly detection. Through comprehensive analysis of behavioral patterns, file system activities, network traffic, and API call sequences, this research identifies key indicators of compromise that enable early ransomware detection before encryption occurs. The findings demonstrate that ensemble machine learning approaches achieve detection rates exceeding 98% with false positive rates below 2%, significantly outperforming traditional signature-based methods. This research contributes to the cybersecurity field by providing a comprehensive framework for implementing AI-driven ransomware defense mechanisms.
This research investigated the application of machine learning and artificial intelligence for ransomware detection and prevention in enterprise network environments. The Hybrid CNN-LSTM model achieved the highest overall performance with 98.7% accuracy, 98.9% precision, 98.5% recall, and a false positive rate of only 1.2%, substantially outperforming traditional signature-based methods. Ensemble methods (XGBoost: 97.8%, Random Forest: 96.4%) provided strong practical alternatives offering superior adversarial robustness and lower computational overhead.
The critical finding that detection latency directly determines protection outcomes — with sub-5-second detection preventing 95% of file encryption — has immediate practical implications for security system design. The dominance of dynamic behavioral features, particularly API call sequences related to file operations and cryptographic functions, reinforces the paradigm shift from static signature-based security to continuous behavioral monitoring. Future research should investigate continuously adaptive ML models, multi-modal detection architectures fusing additional data sources, explainable AI techniques for security transparency, and federated learning for privacy-preserving collaborative defense.
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How to Cite This Paper
Mothanna Abu Judeh, Adnan H. Al-Helali (2026). RansomeWare Detection and Prevention Using Machine Learning and Artificial Intelligence. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
