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@article{174466,
        author = {Sakthidevi I and Selvamani V and Absal K and Arun Siddharth K and Shrivasta G N},
        title = {Real-Time Ransomware Detection and Visualization Framework Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4069-4074},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174466},
        abstract = {This project proposes a real-time ransomware detection and response framework that leverages machine learning for continuous monitoring and an intuitive dashboard for system activity visualization. The framework continuously analyzes system behaviors such as file operations, process behaviors, and resource utilization to identify potential ransomware threats. Machine learning classifiers are employed to detect abnormal patterns indicative of ransomware, triggering automated mitigation processes, including isolation of affected files or systems. A live dashboard offers real-time insights into system health, detected threats, ongoing mitigation actions, and performance metrics, ensuring transparency for users and enabling informed decision-making. The system is designed to be scalable, allowing for the detection of malware types, such as viruses and trojans, through modular updates. Focused on low-latency detection and minimal system impact, the framework ensures efficient operation while maintaining high detection accuracy. Experimental results demonstrate the framework’s effectiveness in detecting and mitigating ransomware attacks in real-time, providing a comprehensive security solution for adaptive defense. This work contributes to the development of proactive malware detection systems, enhancing Ransomware across varied environments.},
        keywords = {Ransomware Detection, Real-Time Monitoring, Machine Learning, Malware Mitigation},
        month = {March},
        }