## 6 Hybrid Vulnerability Scoring Model
### Model Evaluation  
The classification reports provide a detailed breakdown of how well each AI model's survey results align with the actual attack characteristics, leveraging precision, recall, and F1 score to understand performance in multi-class classification, especially with imbalanced classes. \"Support\" indicates the number of actual occurrences for each class. The Gemini Survey results show mixed performance, generally lagging behind other models in overall accuracy across attributes. It struggles significantly with 'Attack Vector' (AV), exhibiting (0%) precision and recall for 'Adjacent' and 'Physical' types, and a lower precision (53.85%) for 'Network' despite high recall (100%). The model completely misses predicting 'None' for 'Attack Type' (AT) and 'High' for 'Privilege Required' (PR), as well as 'Low' for 'Confidentiality' (C) and 'Low' and 'None' for 'Integrity' (I), indicated by (0%) recall for these categories. This suggests that Gemini tends to be overly cautious or biased towards predicting certain common classes, leading to poor performance on less frequent or minority classes. Copilot demonstrates a more balanced and generally stronger performance compared to Gemini, with robust overall accuracies. It shows strong predictive capabilities in 'Att \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision (I)** & **Precision (I)** & **Recall (I)** & **Recall (I)** & **F1 Score (I)** & **F1 Score (I)** & **Macro F1** \\ \hline TF-IDF + Logistic Regression & 0.87 & 0.95 & 0.78 & 0.86 & 0.81 & 0.96 & 0.88 & 1.8 \\ USE & 0.70 & 0.72 & 0.66 & 0.69 & 0.69 & 0.74 & 0.71 & 211 s \\ SVM (TF-IDF) & 0.87 & 0.93 & 0.80 & 0.86 & 0.82 & 0.94 & 0.88 & 1 s \\ Best Tuned USE (Threshold = 0.5) & 0.63 & 0.69 & 0.48 & 0.56 & 0.80 & 0.78 & 0.68 & 51 s \\ Tuned USE (Custom Threshold) & 0.64 & 1.00 & 0.28 & 0.44 & 0.58 & 1.00 & 0.74 & 1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Model performance on balanced dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision (I)** & **Recall (I)** & **F1 Score (I)** & **Precision (I)** & **Recall (I)** & **F1 Score (I)** & **Time** \\ \hline TF-IDF + Logistic Regression & 0.87 & 0.95 & 0.78 & 0.86 & 0.81 & 0.96 & 0.88 & 1.8 \\ USE & 0.70 & 0.72 & 0.66 & 0.69 & 0.69 & 0.74 & 0.71 & 211 s \\ SVM (TF-IDF) & 0.87 & 0.93 & 0.80 & 0.86 & 0.82 & 0.94 & 0.88 & 1 s \\ Best Tuned USE (Threshold = 0.5) & 0.63 & 0.69 & 0.48 & 0.56 & 0.80 & 0.78 & 0.68 & 51 s \\ Tuned USE (Custom Threshold) & 0.64 & 1.00 & 0.28 & 0.44 & 0.58 & 1.00 & 0.74 & 1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Model performance on imbalanced dataset. tack Vector' (AV) and 'Availability' (A), particularly excelling in 'Physical' AV with (80%) precision and (100%) recall. For 'User Interaction' (UI), it achieves (90.91%) precision for 'None'. However, Copilot also exhibits weaknesses in specific areas, struggling with 'Low' AC ((60%) precision, (42.86%) recall) and completely missing 'High' PR and 'Required' UI ((0%) recall). 'Confidentiality' (C) shows good recall for 'High' and 'Low' but very low precision for 'Low' (25.00%). DeepSeek's survey results stand out as the strongest performer among the evaluated models, consistently achieving higher overall accuracies. It demonstrates strong predictive capabilities for 'Attack Complexity' (AC) and 'Confidentiality' (C), particularly for 'High' C ((85.71%) precision, (75%) recall) and 'None' C ((100.00%) precision, (83.33%) recall). For 'Attack Vector' (AV), it accurately identifies 'Network' with (100%) recall. Nevertheless, DeepSeek also faces challenges with minority classes like 'Adjacent' AV ((0%) precision and recall) and 'High' PR ((0%) precision and recall). ChatGPT's survey results show performance that is generally better than Gemini but not as strong or consistent as Copilot or DeepSeek. It performs exceptionally well for 'User Interaction' (UI) for the 'None' class ((92.31%) precision, (85.71%) recall).