A Review of Ransomware Detection Models for Cybersecurity Driven IIoT in Cloud Environments

Ransomware is currently one of the most severe cybersecurity threats and not only attacks legacy systems but cloud systems and Industrial Internet of Things (IIoT) systems as well. Security and privacy threats are heightened as these systems integrate more closely and thus are exposed to sophisticated and long-lasting attacks. This paper provides a comprehensive review of ransomware prevention and detection measures in cloud and IIoT environments with an emphasis on the usage of Machine Learning (ML) and Deep Learning (DL) models. Research studies published across IEEE, Elsevier, and Springer databases between 2020 and 2024 were analyzed. Our check reveals Ensemble methods and Random Forest (RF) are two of the ML methods most in use, with each at 18.00%, followed by Neural Networks (NNs) at 12.00%, with older models such as Support Vector Machines (SVMs) with 10.00%, Naïve Bayes (NBs) had 7.00%, and Decision Trees (DTs) still in use with utilization at 9.00% . Additionally, DL approaches (including Convolutional NN (NN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Recurrent NN (RNN)) account for 20.00% of the techniques deployed, highlighting their growing prominence in IIoT security and ransomware research. Indicative of their integration into hybrid ML pipelines, Light Gradient Boosting Machine (LightGBM) and other ensemble boosting frameworks comprise 16.00%. Last but not least, other novel and specialized models including Extreme Gradient Boosting (XGBoos), Self-Organizing Maps (SOM), Gain Ratio, and Digital DNA account for 8.00% of the overall utilization observed throughout study. Among DL methods, Recurrent NNs (RNNs) are at the forefront with 40%, followed by CNNs with 30%, CNN–RNN hybrid models at 20%, and Autoencoders with 10%. Integration of cryptographic schemes, federated learning, blockchain-based audit mechanisms, and adaptive runtime mechanisms have further boosted the mechanisms of anomaly detection with detection rates of over 99% for polymorphic and zero-day ransomware.

@article{Ali2025,
  title     = {A Review of Ransomware Detection Models for Cybersecurity Driven IIoT in Cloud Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Abrar Ali and Norah Hamed and Monir Abdullah},
  doi       = {10.14569/IJACSA.2025.0161039},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161039}
}