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@article{193667,
        author = {Tatikonda Meghana and Yandrapragada Saroja and Banda Ramya and Uppara Nagamani and Usrupati Anjali and Dr. G K V Narasimha Reddy and Dr. C V Madhusudhan Reddy},
        title = {Ransomware Early Detection Engine Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {969-972},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193667},
        abstract = {Ransomware has emerged as one of the most serious cybersecurity threats in modern digital environments, causing extensive financial losses and operational disruption by encrypting critical data and demanding ransom payments. Conventional security solutions that rely primarily on signature-based detection methods often struggle to identify newly evolving ransomware variants and zero-day attacks. As a result, organizations require more intelligent and proactive detection mechanisms capable of identifying malicious behavior at an early stage.
This project proposes a ransomware early detection engine based on machine learning techniques that monitors real-time system activities and identifies abnormal behavior patterns. The system analyzes multiple behavioral indicators including file access patterns, process execution activities, registry modifications, API calls, and network traffic to detect potential ransomware activity. Machine learning classifiers such as Random Forest, Support Vector Machine (SVM), and Gradient Boosting are trained using behavioral datasets to distinguish between malicious and benign activities.
Feature extraction, data preprocessing, normalization, and model optimization techniques are applied to enhance classification accuracy while minimizing false positives. The proposed framework enables early detection of ransomware before extensive file encryption occurs. This solution can be deployed in enterprise networks, endpoint security systems, cloud environments, and critical infrastructure platforms, providing a scalable and intelligent defense against modern ransomware threats.},
        keywords = {Ransomware Detection, Machine Learning, Cybersecurity, Behavioral Analysis, Malware Detection, Network Security, Anomaly Detection.},
        month = {March},
        }