A Robust Machine Learning Method for Intelligent Ransomware Identify in Industrial Control Networks

Abstract

Malicious software known as Malware in the form of viruses, ransomware, and spyware has turned into a global epidemic, and research shows that the impact is intensifying. Numerous ways have been introduced to date to deal with these hazards. To handle this increasing problem, this paper proposes an effective Deep Neural Network (DNN) model that can be used to detect ransomware precisely. The model proves to be very effective in the separation of malicious and benign samples, with an accuracy, precision, recall, and F1-score of 99.76, and an AUC value of 0.98, which indicates the close to perfection of the classification. The findings reveal the high learning stability and generalization without overfitting that is reinforced by the stable training and validation. Compared to the current methods, including KNN (83.9%), VGG-16 (90.5%), XGBoost (94.1%), and Logistic Regression (96%), the DNN-based model was better in its performance. On the whole, this paper highlights how deep learning can be used to reinforce cybersecurity protection and offer a scalable and intelligent method to counter the ransomware attacks in the present digital environment.