Ransomware Detection Using Network Traffic Patterns: A Hybrid Approach with Isolation Forest and Gradient Boosting

The escalating prevalence of ransomware attacks, characterized by their complex and adaptive nature, poses significant threats to the integrity and availability of critical network infrastructures. As malicious actors continue to refine their techniques, traditional detection methods often prove insufficient, necessitating the exploration of more advanced and dynamic approaches to identify these threats promptly and accurately. In response to this pressing issue, a novel hybrid model has been developed, which synergistically combines the strengths of anomaly detection and classification algorithms to enhance the identification of ransomware activities. This approach is particularly significant due to its ability to effectively adapt to both known and unknown variants of ransomware, utilizing a comprehensive analysis of network traffic patterns. By integrating unsupervised techniques, such as Isolation Forest, with the classification prowess of Gradient Boosting, the model achieves a notable improvement in detection accuracy while simultaneously reducing the rate of false positives. This innovative framework not only streamlines the cybersecurity workflow but also bolsters the resilience of networks against increasingly sophisticated attacks, thus representing a crucial advancement in the ongoing battle against ransomware.