# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results
### Comparison of Different Fusion Strategies  
The fusion layer plays a crucial role when both data modalities are available as a given input instance, enabling the system to combine predictions from each independent model to produce a final decision. The current section of the current work evaluates and compares the performance of multiple decision-level fusion strategies used to integrate outputs from the image and tabular branches of the proposed threat detection pipeline. To this end, we selected and evaluated three distinct fusion methods, as indicated within Section 3.5: Majority Voting, Rule-Based (Boolean Logic), and Confidence-Weighted. Each strategy was applied to a subset of test samples that included both image-based and tabular-based data, enabling multimodal fusion under realistic simulation environments. The goal was to measure the efficiency of each method in terms of threat detection accuracy, precision, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model (Ref)** & **Task Domain** & **Dataset(s) Used** & **Accuracy** & **F1-Score** & **Precision** & **Recall** & **AUC** & **Key Techniques** \\ \hline PlausMal-GAN [18] & Malware Detection (Image) & Malimg & 0.964 & 0.910 & 0.916 & 0.910 & - & Ensemble of LSGAN, \\  & Botnet Detection & Kaggle Microsoft & & & & & DCGAN, WGAN-GP, E-GAN \\ DPGAN [34] & Detection Detection (Image) & Malware Classification Challenge & 0.970 & 0.879 & 0.902 & 0.858 & - & Deep Convolutional GAN (DCGAN) + Differential Privacy \\  & Malware Detection (Image) & Challenge & **0.972** & **0.973** & **0.975** & **0.971** & **0.991** & **DCGAN-based malware image generation + CNN** ensemble \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison Summarization against recent Malware Image Detection GANs Models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model (Ref)** & **Task Domain** & **Dataset(s) Used** & **Accuracy** & **F1-Score** & **Precision** & **Recall** & **AUC** & **Key Techniques** \\ \hline TransGAN-ID [22] & Intrusion Detection & CIC-IDS2017 & 0.916 & 0.952 & 0.912 & 0.969 & - & GAN + Transformer \\  & & Zeno-day Attacks & FEMD & - & 0.933 & - & - & 0.965 & Few-shot Learning Conditional Generative Adversarial Network \\ WCGAN + XGBost & Intrusion Detection Detection & UNSW-NB15 & - & 0.876 & 0.867 & 0.885 & - & Conditional Wasserstein GAN + XGBoost classifier \\
**Ours–Zero-Day** & **Zero-Day Threat** & **CTU-13 (bottet + background)** & **0.945** & **0.946** & **0.952** & **0.941** & **0.982** & **ZDGAN + ensemble voting + transfer learning** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison Summarization against a set of recent Tabular GANs Models. recall, F1-score, and false positive rate when both predictions are integrated rather than taken from a single branch individually. Table 17 highlights the comparative results for each algorithm, respectively. As it's obvious, among the methods tested, Confidence-Weighted Averaging outperformed the others across the most key evaluation metrics, achieving a superior balance between precision and recall, as well as the lowest false positive rate. These results confirm the effectiveness, robustness, and operational feasibility of dynamically weighting predictions based on the relative confidence levels of each model's certainty, especially in heterogeneous data environments. Figure 10 illustrates the F1-score and AUC performance of each strategy, highlighting the performance results of probabilistic combination techniques over simpler rules like majority voting or Boolean logic. The evaluation confirms that the integrative framework can adapt to multiple data formats and leverage synthetic augmentation to strengthen detection robustness, while the selected fusion mechanism plays a key role in enhancing decision-making's reliability across the framework. 1.