Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ritesh; Rathor, Arun; Chinnaiyan, R.; Ragavendra, T.S.; Sabarmathi, G.; Khanna, Vimal Kumar Title A name given to the resource Ransomware Detection using Dynamic Behavior Monitoring based on Entropy Analysis and Frequency Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234519" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234519</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030092216?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030092216?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ritesh, Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Rathor A., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Chinnaiyan R., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Ragavendra T.S., S-Vyasa School of Advanced Studies, Department of Computer Science and Engineering, Bengaluru, India; Sabarmathi G., Christ University, School of Computer Science, Bangalore, India; Khanna V.K., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India Description An account of the resource Cybersecurity faces mounting challenges due to the proliferation of ransomware, a sophisticated form of malware that encrypts user data, rendering it inaccessible unless a ransom is paid. Traditional detection systems often fail to counteract evolving threats effectively, creating an urgent need for innovative approaches. Introducing a novel hybrid framework for ransomware detection within IoT ecosystems, integrating entropy and frequency analysis with machine learning models, including Decision Trees (DT) and Random Forests (RF). Data augmentation techniques were employed to generate synthetic data, bolstering the models' ability to generalize across diverse scenarios. Experimental results demonstrated superior performance of the DT classifier, achieving an accuracy of 98.89% and an F1-score of 98.81%. The proposed framework is optimized for real-time ransomware detection, leveraging dynamic analysis to monitor live system behaviors. This integration ensures a proactive defense mechanism against emerging ransomware variants. Future research directions include expanding real-time capabilities, enhancing cross-layer detection, and for collaborative threat intelligence. This work represents a significant advancement in ransomware detection methodologies, offering robust, adaptive, and scalable solutions to mitigate one of cybersecurity's most pressing threats. 2025 IEEE. Subject The topic of the resource Entropy analysis; Frequency Analysis; IOT ecosystem; Ransomware Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online