## 6 Hybrid Vulnerability Scoring Model
### Proposed Model
The expert input ensured contextual accuracy across the full attribute set, including attack complexity, while the machine learning component contributed additional scale and automation specifically for that single attribute. The final values for all attributes were determined based on expert evaluation, and only the attack complexity attribute incorporated machine learning input as a secondary reference. These expert-evaluated attributes were then input into a CVSS v4.0-based scoring algorithm to compute a composite base score for each identified vulnerability. This score reflects a multi-dimensional assessment that is context-aware, domain-informed, and guided by human expertise. The model remains modular and extensible, allowing for the future inclusion of AI components or custom weighting schemes as needed. By grounding automated assistance in expert oversight, this approach enhances accuracy, preserves contextual relevance, and supports scalable, real-time security evaluation in safety-critical domains such as autonomous vehicles. To validate the performance and practical utility of the attack complexity classifier within the hybrid model, a series of experiments were conducted comparing multiple machine learning models on both imbalanced and balanced datasets. Several classifiers were evaluated, including traditional approaches such as Logistic Regression and Support Vector Machines (SVM), the Universal Sentence Encoder (USE), and Deep Learning models like BERT. The goal was to assess each model's predictive power, particularly in handling the contextual nuances required for classifying attack complexity, while also evaluating computational efficiency. The initial dataset exhibited a significant class imbalance, with 3350 samples labeled as LOW and only 310 as HIGH, resulting in an imbalance ratio of approximately 10.81:1. The performance of each model on this imbalanced dataset is summarized in Table 11.  
As shown, BERT achieved the highest accuracy (0.846), marginally outperforming traditional models such as TF-IDF + Logistic Regression and SVM (both at 0.821). However, BERT's training time ( 30 min) was substantially higher than that of traditional models, which completed training in about one second. To explore the effects of class balance, a second experiment was conducted using a balanced dataset of 500 samples, equally split between the two classes. As summarized in Table 12, traditional models again performed strongly. Both SVM and TF-IDF + Logistic Regression achieved an accuracy of 0.87 and high F1 scores, while maintaining minimal training times. In contrast, the USE models demonstrated varied performance, and tuning efforts did not consistently yield improvements. These findings underscore three key insights: first, that addressing class imbalance is essential to model fairness and performance; second, that traditional machine learning models remain strong contenders, particularly in resource-constrained environments; and third, that complex models like BERT, while powerful, may not always justify their computational demands in every application context. Ultimately, this evaluation supports the use of traditional machine learning models for the Attack Complexity classifier in the hybrid Human-AI scoring model. When combined with expert input, these models provide a reliable, efficient, and context-sensitive mechanism for assessing cyber vulnerabilities--especially in safety-critical domains like autonomous vehicles.