Cyber Risk Prediction and Management Using Random Forest-Based Risk Scoring Models

The increasing relevance and number of sophisticated cyber threats are creating the need for the development of intelligent and robust systems for risk prediction and management. The paper introduces an elaborate framework for cyber risk assessment and mitigation through Random Forest risk scoring models. In their research, the authors used a Random Forest classifier to train the model with past cyber incidents' data and system attributes. This approach enabled the model to effectively recognize and predict cyber risks. The system applies risk scores to the various system parts, which greatly simplifies the identification of vulnerabilities. Feature selection is executed to point out the attributes of the most influence on cyber risk and make the model more transparent and efficient. The system also introduces a dynamic risk management module to adapt to changing threat landscapes by keeping the model up to date with new data. Our experimental results demonstrate robust performance with 94.8% accuracy, 92.3% precision, and 93.7% recall, proving the system's effectiveness and reliability in real-world cybersecurity environments. The paper presents as follows: one of the priority points is the inclusion in the machine-learning-based scoring method for the quantification of cyber risks, a feature-optimized Random Forest classifier for risk prediction and a feedback-based mechanism of risk model updating. This framework grants organizations a mechanism for the early detection of risks, cybersecurity decision-making, and finally, the safety of the data.