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Title	Smart City Cybersecurity: Leveraging Machine Learning For Advanced Ransomware Detection And Prevention
ID_Doc	50142
Authors	Prajapati Y.; Suthar O.P.; Gosai K.; Singh S.K.
Year	2025
Published	2025 International Conference on Pervasive Computational Technologies, ICPCT 2025
DOI	http://dx.doi.org/10.1109/ICPCT64145.2025.10941048
Abstract	Data encryption and extortion schemes are hallmarks of ransomware attacks, which have recently arisen as a major cybersecurity concern. The sophistication and frequency of these attacks are increasing, and conventional detection technologies are finding it harder to keep up. Using sophisticated algorithms to spot unusual or suspicious patterns in system activity, this research lays forth a machine learning-based strategy for ransomware attack detection. The suggested model finds ransomware early, before it can do significant damage, by examining both static and dynamic aspects of file activity, network traffic, and system functions. We investigate the efficacy of numerous ML models, comparing their recall, accuracy, and precision. These models include support vector machines, Random Forest, and XGBoost. The findings show that Machine-learning provides a viable and effective technique for identifying ransomware in real-time. Our Method demonstrate 97.333% highest accuracy that leads to classify in two different classes. Along with future research directions for strengthening machine learning models against ransomware variants, we also address problems relating to availability of data, feature selection, and model generalization. © 2025 IEEE.
Author Keywords	Cybersecurity; Extreme Gradient Boosting; Feature Selection; Machine Learning; Ransomware Classification

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