EARLY-STAGE RANSOMWARE DETECTION AND PREVENTION USING BEHAVIOUR AND NETWORK TRAFFIC ANALYSIS

The Early-Stage Ransomware Detection and Prevention System successfully provides an effective solution for identifying and preventing ransomware attacks before significant damage occurs. By combining behaviour analysis, network traffic monitoring, anomaly detection, and machine learning techniques, the system can detect suspicious activities in real time and respond quickly to potential threats. The proposed system continuously monitors file operations and network communications, enabling the early identification of ransomware behaviour. The use of the Extra Trees machine learning algorithm improves detection accuracy and helps distinguish malicious activities from normal system behaviour. In addition, the prevention module automatically blocks suspicious connections, terminates malicious processes, and generates real-time alerts to enhance system security. The experimental results demonstrate that the system can effectively reduce the impact of ransomware attacks, minimize data loss, and improve overall cybersecurity protection. The user-friendly interface further enhances usability by providing timely notifications and threat information. In the future, the system can be enhanced by incorporating advanced deep learning techniques, cloud-based threat intelligence, real-time backup and recovery mechanisms, and support for detecting a wider range of cyber threats. Overall, the proposed system offers a reliable, scalable, and efficient approach to protecting modern computing environments from ransomware attacks.

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