Next-Generation Ransomware Defense Using LSTM-Powered Behavioral Analytics

Ransomware has rapidly become one of the most hazardous cybersecurity threats due to its ability to encrypt the data of the users and disrupt the business activities of other countries. They need intelligent and adaptable solutions due to the fact that traditional rule-based and signature-based detection systems are often ineffective to detect new and polymorphic variants. This paper reports an AI-based ransomware detection framework based on a Long Short-Term Memory (LSTM) network to analyze sequence of file operations to identify suspicious actions. In order to reproduce the benign and ransomware-induced file system activity, i.e. create, read, write, rename and delete files, a synthetic but realistic dataset was constructed. Every event is enclosed with behavioral indicators such as file entropy, access frequency, accessible and inaccessible size of the file, and intervals between timestamps. In order to get the temporal dependencies among event sequences, the proposed model will integrate the normalized numerical features, as well as embedded categorical features. Experimental analyses of the model on the basis of precision-recall analysis, ROC curve, and confusion matrix confirm that the model has a high detection accuracy of more than 98% and generalizes well on unseen data. Also, to make entropy evolution visualization more interpretable, the distinction between processes driven by ransomware and ordinary ones can be made. The proposed LSTM-based system is effective in the sense that it is capable of identifying sequential relationships as opposed to traditional machine learning algorithms, which enables it to identify ransomware early and proactively. This study indicates the potential of behavioral intelligence that operates on deep learning to develop strong and real-time cybersecurity defense systems.