Ransomware Detection and Mitigation using Behavior-based Analysis

Ransomware is one of the most formidable cyber threats, especially in the cloud environment, where traditional signature-based identification methods are usually reduced. This research presents a new hybrid ransomware defence system (HRDS) that takes advantage of behavioural analysis and machine learning to detect and reduce real-time ransomware attacks.The system is part of a double-stage eating pipeline: a random forest classifier for rapid static resource analysis and a long short-term memory network (LSTM) for dynamic sequence evaluation. Behavioural data such as file entropy, CPU use peaks, process standards, and registration changes are monitored and analysed to distinguish benign activity from malicious behaviour. After detection, the system begins a mitigation protocol through a dynamic encryption health module (DEHM) that interrupts suspicious processes, blocks Blocking I/O Operations and restores affected files using volume shade copies. Experimental results show a 98.4% detection accuracy with low false positive rates (1.6%) and an average response time of 5.8 seconds. A case study using a simulated Ransomware Locky attack validates the effectiveness of the proposed system. This approach demonstrates strong, scalable and adaptable protection potential against ransomware evolution in corporate and cloud-based environments.