## 4 Experimental Results
### Discussion Summary  
It has been proven that the TOPSIS methodology can effectively be leveraged to select the most affected features, and this, in turn, increases the security level of the authentication system. The three extracted features, velocity, X coordinate, and touch pressure, have been recognized to be the most affected and valuable features and can significantly elevate the classification accuracy. As previously explained via the experimental findings, the hybrid ML models and these affected features have refined the performance of the system significantly. It has been pointed out that when the RF combined with the TOPSIS methodology-based feature was chosen, it can accomplish the highest accuracy compared to the two other algorithms. Under different environmental circumstances and conditions, e.g., device orientation and modifications in lighting density, our proposed technique has been carefully evaluated in terms of resiliency and robustness with minimal performance penalty and high detection rates in spoofing attempt-based attacks. Moreover, the proposal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Scenario** & **Metric** & **Result** \\ \hline Spoofing detection & Detection rate & 96\% \\ Lighting variations & Accuracy & 92.3\% \\ Device orientation changes & Accuracy & 93.2\% \\ Noise injection in data & Accuracy & 90.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testing the resiliency of the proposed technique-based RF model against four serious cybersecurity threats.  
keeps achieving a high level of accuracy, underscoring the combined strength of the chosen features and robustness's model in refining both accuracy and reliability in real-world applications and real system processing even when noise was inserted into the original dataset. Although the applied perturbation methods proved useful in enhancing model robustness, it is acknowledged that their parameters were heuristically defined. The absence of a detailed sensitivity analysis may limit the understanding of how perturbation intensity or scaling affects classification accuracy and stability. Future work will address this gap by systematically evaluating a range of perturbation settings and quantifying their impact on model performance.  
Even though the proposed framework has shown many advancements, it also has some limitations. For example, environmental conditions and highly dependency on variations in device orientation and lighting density can slightly impact on collected and generated datasets. Although the dataset consisted of 30 participants, this sample size is considered relatively small and may not fully capture the diversity of behavioral biometric patterns observed in real-world settings. To enhance generalizability, future work will involve expanding the dataset by including a broader range of participants from different groups and evaluating the system across multiple smartphone platforms. However, it should be noted that the evaluation was conducted using a single smartphone model (Samsung Galaxy A72), which may limit the generalizability of the results across different hardware platforms. To address this limitation, future work will include validating the proposed framework on multiple smartphone devices with diverse hardware and sensor configurations to better assess its practical applicability under real-world conditions. Finally, significant additional resources might be requested in some of the designed and implemented ML algorithms, e.g., GBM, and this, in turn, potentially impacts both the computational penalty and the real-time performance.