A survey on ransomware detection using AI models

Goteti Badrinath, Arpita Gupta

Abstract


Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to improve ransomware detection capabilities. It classifies ransomware types, highlights active groups such as Akira, and evaluates new DL techniques effective at real-time data analysis and encryption handling. Feature extraction, selection methods, and essential parameters for effective detection, including accuracy, precision, recall, F1-score and receiver operating characteristic (ROC) curve, are identified. The findings point to the state of the art and the state of the art in AI based ransomware detection and underscore the need for robust, real-time models and collaborative research. The statistical and graphical analyses help researchers and practitioners understand existing trends and directions for future development of efficient ransomware detection systems to strengthen cybersecurity in data centers and cloud infrastructures.

Keywords


Artificial intelligence; Cloud security; Deep learning; Encryption; Machine learning; Ransomware; Real-time detection

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DOI: http://doi.org/10.11591/ijict.v14i3.pp1085-1094

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Copyright (c) 2025 Goteti Badrinath, Arpita Gupta

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The International Journal of Informatics and Communication Technology (IJ-ICT)
p-ISSN 2252-8776, e-ISSNĀ 2722-2616
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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