## 4 Discussion
While the proposed Transformer-GAN-INSBBO framework demonstrates strong performance in experimental settings, several deployment challenges must be addressed before \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Proposed Models**} & \multicolumn{3}{c}{**Binary Classification**} & \multicolumn{2}{c}{**Multi-Class Classification**} \\ \cline{2-5}  & \(p\)**-Value** & **Results** & \(p\)**-Value** & **Results** \\ \hline Transformer-GAN vs. Transformer & 0.0006 & Significant & 0.0008 & Significant \\ Transformer-GAN vs. GAN & 0.0008 & Significant & 0.0009 & Significant \\ Transformer-GAN vs. LSTM & 0.00004 & Significant & 0.00008 & Significant \\ Transformer-GAN vs. CNN & 0.00007 & Significant & 0.00009 & Significant \\ Transformer-GAN vs. SVM & 0.000005 & Significant & 0.000008 & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical significance results from \(t\)-tests comparing transformer-GAN to baseline models. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Proposed Models**} & \multicolumn{2}{c}{**Standard Deviation**} \\ \cline{2-3}  & **Binary Classification** & **Multi-Class Classification** \\ \hline Transformer-GAN (INSBBO) & 0.0000365 & 0.0006325 \\ Transformer and GAN & 0.0423691 & 1.0569851 \\ Transformer & 0.8325147 & 1.3201459 \\ GAN & 0.9632152 & 1.9632453 \\ LSTM & 1.9652314 & 2.2145963 \\ CNN & 3.2415692 & 4.3215963 \\ SVM & 5.2963214 & 7.2145368 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Standard deviation values of classification results across multiple runs for different models. real-world integration. First, latency poses a significant concern, especially in time-sensitive applications such as industrial control or healthcare. Although the transformer-based architecture enables parallel processing, the computational overhead of GAN training and INSBO optimization can impact real-time detection capabilities. Second, scalability remains a practical barrier, as IoT networks may consist of thousands of heterogeneous devices generating high-volume traffic. Efficient model compression and edge-friendly inference strategies may be required to ensure operational feasibility. Third, device heterogeneity, stemming from differences in protocols, hardware capabilities, and operating systems, can cause inconsistencies in data distributions and hinder generalization. Although the proposed model was validated on diverse datasets, domain adaptation techniques or transfer learning may be necessary to maintain robustness across varied deployment environments. Future work will explore lightweight model variants and edge deployment strategies to mitigate these limitations. Furthermore, it is worth noting that in real-world IoT traffic, temporal and contextual dependencies are crucial for detecting multi-stage and stealthy attacks that evolve over time. For example, in the CIC-IoT-2023 dataset, a reconnaissance attack may begin with a sequence of benign DNS and ICMP packets, followed by suspicious port scanning behavior minutes later. Capturing such patterns requires a model that can retain long-term dependencies and associate earlier benign-looking events with later malicious activities. Traditional models like CNN or shallow RNNs often fail to link these temporally dispersed patterns. The Transformer architecture, with its self-attention mechanism, is well-suited to capture these long-range dependencies, making it particularly effective for modeling complex IoT attack sequences that span multiple time windows.