Enhancing TARA Through a Continuous Improvement Method Based on Cybersecurity Threat Intelligence

The ever-increasing complexity and dynamics of cyber threats demand risk assessment tools that extend beyond the realm of static and periodic risk assessments. Although Threat Analysis and Risk Assessment (TARA) techniques are systematic, they are not flexible enough to handle dynamic cyber threats. To overcome the limitations of the conventional Threat Analysis and Risk Assessment techniques, this paper proposes a continuous improvement approach that incorporates Cybersecurity Threat Intelligence (CTI) and an intelligent risk prediction model. In this paper, a hybrid multimodal ensemble approach has been proposed to predict the CVSS score based on the structured vulnerability attributes and unstructured textual threat intelligence obtained from the CVE data. Comprehensive experiments have been performed using the large-scale CVE dataset to prove the efficiency of the proposed approach in efficiently predicting the risk with an R² value of 0.9947 and a Mean Absolute Error of 0.0133. Explainability analysis is performed to ensure that the proposed approach meets the predefined cybersecurity risk principles. The experimental outcome clearly demonstrates that the proposed CTI-based continuous TARA approach can enhance the accuracy of risk prediction in a dynamic cybersecurity setting.