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@article{201012,
        author = {Ms. M. Mohana Priya and Ramya. B and Nisha. R and Shalini. T},
        title = {Intelligent Ransomware Detection and Early Warning System Using Hybrid Machine Learning and Real-Time Behavioral Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {157-164},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201012},
        abstract = {Ransomware has become one of the most destructive cybersecurity threats, causing financial losses and data breaches across organizations globally. Traditional antivirus and signature-based detection systems have proven ineffective against new and evolving ransomware variants. This research proposes an Intelligent Hybrid Machine Learning-based Ransomware Detection and Autonomous Defense System that integrates real-time behavioral monitoring with advanced machine learning algorithms. The system monitors critical system activities including file modifications, entropy changes, CPU usage, and process activity patterns. A hybrid detection approach combining Random Forest for known threat detection and Isolation Forest for anomaly-based zero-day ransomware detection is implemented. Upon detecting suspicious behavior, the system automatically initiates defensive mechanisms including process termination, emergency file backup, network isolation, and forensic logging. The proposed system achieves high detection accuracy while maintaining low false positive rates. Real-time monitoring and autonomous response capabilities significantly reduce ransomware damage and recovery time. Experimental evaluation demonstrates superior performance compared to traditional signature-based and rule-based detection systems.},
        keywords = {Ransomware Detection, Behavioral Analysis, Machine Learning, Random Forest, Isolation Forest, Real-Time Monitoring, Autonomous Defense.},
        month = {May},
        }