## 6 Hybrid Vulnerability Scoring Model
### Model Evaluation
#### 4.2.4 Discussion and Conclusion:  
As shown in Figure 8, and Table 21, the hybrid AI model demonstrates promising performance in automating CVSS scoring, particularly for impact-related metrics such as Availability, Confidentiality, and User Interaction. However, performance drops for more context-dependent attributes like Privileges Required, suggesting limitations in the LLM interpretability of implicit assumptions and access conditions. The analysis of metric-level prediction accuracy reveals key strengths and weaknesses in the current CVSS component prediction model. The model demonstrates strong performance in identifying User Interaction (93.3%), Availability (86.7%), and Confidentiality (80.0%), suggesting a solid understanding of direct impacts on system behavior and user involvement. However, noticeable gaps emerge in metrics such as Privileges Required (46.7%), Attack Complexity (60.0%), and Attack Requirements (60.0%), indicating challenges in assessing contextual and precondition-based factors that influence exploitability. These \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attack** & **ChatGPT** & **DeepSeek** & **Copilot** & **Gemini** & **Hybrid** & **Expert** & **Match** \\ \hline Blinding Attack & None & None & High & None & None & None & True \\ Jamming Attack & None & Low & Low & None & None & Low & False \\ Black Hole Attack & None & Low & High & None & None & High & False \\ Black Hole Attack & None & Low & High & None & None & High & False \\ Timing Attack & High & Low & High & High & High & High & True \\ Disruptive Attack & None & None & Low & None & None & None & True \\ Replay Attack & None & High & High & High & High & True \\ Relay Attack & None & High & High & High & High & True \\ Easydropping Attack & High & High & High & High & High & True \\ Sybil Attack & None & High & High & High & High & True \\ Blind Spot Exploitation & None & None & Low & None & None & None & True \\ Sensor Interference Attack & None & None & Low & None & None & True \\ Sensor Interference Attack & None & None & Low & None & None & True \\ Acoustic Attack & None & None & None & Low & None & True \\ Acoustic Attack & None & High & High & High & High & True \\ Impersonation Attack & None & High & High & High & High & True \\ Falsified Information Attack & None & High & High & High & High & True \\ Cloaking Attack & None & High & High & High & High & True \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Integrity classification results: LLMs vs. expert labels. To evaluate the overall accuracy and practical applicability of the hybrid model, a comparison using the heatmap was made between the CVSS base scores derived from the model and those determined by domain experts. This heatmap provides a comparative visual of four key metrics--Hybrid CVSS, Expert CVSS, standard deviation, and residuals--across 15 different attack scenarios. As shown in Figure 9, the Hybrid CVSS values demonstrated strong alignment with expert assessments across the 15 evaluated attacks. The average CVSS score assigned by the hybrid model was found to be consistent with the expert mean scores in most cases, despite minor discrepancies. Notably, in several high-impact attacks--such as those scoring 9.2 and 8.7 from the hybrid model--the expert evaluations were slightly lower but still within the expected range when considering the standard deviation (ranging from 1.1 to 1.9). For instance, an attack scored at 9.2 by the hybrid model corresponded to an expert mean of 6.35 with a standard deviation of 1.7, placing the hybrid result within approximately 1.7 standard deviations of expert consensus, which is statistically acceptable. Furthermore, the overall trend indicates that the hybrid model tends to slightly overestimate the severity compared to expert evaluations. However, considering the expert score variability (standard deviations averaging around 1.5), this bias remains within a tolerable and explainable range.