Ransomware Threat Detection and Mitigation using Machine Learning Models

The relentless evolution of ransomware threats poses an increasingly severe challenge to cybersecurity. In response to this growing menace, this study presents a comprehensive approach for Ransomware Threat Detection and Mitigation using Machine Learning Models. Leveraging the power of advanced machine learning techniques, the proposed method seeks to revolutionize the landscape of ransomware defense. Our methodology commences with the collection and preprocessing of diverse datasets encompassing system and network activities. Feature engineering techniques extract relevant behavioral attributes. A trio of machine learning algorithms, including Self-Organizing Maps (SOM), Random Forest Classifier, and Long Short-Term Memory (LSTM) networks, is deployed for behavioral analysis. These algorithms excel in identifying intricate patterns and temporal dependencies within the data. Threshold-based classification ensures efficient identification of potential threats. Automated response mechanisms further fortify the system, enabling rapid isolation of affected systems and termination of malicious processes. Performance evaluation reveals the superiority of the proposed method over traditional approaches. High Detection Accuracy (93.0%) and Precision (97.0%) attest to its efficacy in accurate ransomware threat identification while minimizing false alarms. The use of LSTM networks bolsters sequential behavior analysis, a crucial factor in combating evolving ransomware tactics. The Proposed methodology's adaptability shines through its capacity to continuously learn and evolve with new data, thereby staying ahead of emerging threats. The inclusion of automated responses adds a layer of proactive defense, crucial in today's fast-paced threat landscape.