ItemConference Paper

Ransomware Detection using Dynamic Behavior Monitoring based on Entropy Analysis and Frequency Analysis

Title
Ransomware Detection using Dynamic Behavior Monitoring based on Entropy Analysis and Frequency Analysis
Creator
Ritesh; Rathor, Arun; Chinnaiyan, R.; Ragavendra, T.S.; Sabarmathi, G.; Khanna, Vimal Kumar
Description
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.
Source
2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Entropy analysis; Frequency Analysis; IOT ecosystem; Ransomware
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159610-1;
Format
online
Language
English
Type
Conference paper

Citation

Ritesh; Rathor, Arun; Chinnaiyan, R.; Ragavendra, T.S.; Sabarmathi, G.; Khanna, Vimal Kumar, “Ransomware Detection using Dynamic Behavior Monitoring based on Entropy Analysis and Frequency Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25921.

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