# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 5 Conclusions and Future Work  
The current study presented an integrative framework for advanced cybersecurity threat detection that combines GAN-based synthetic data generation, modality-specific classifiers, and decision-level fusion strategies. Through a modular and adaptable architecture, the system efficiently processes both image-based malware samples and structured tabular network intrusion data. Experimental results demonstrated high detection accuracy performance for each individual branch, particularly 97.2% for the CNN-based malware detector and 94.5% for the ensemble tabular classifier. In parallel, the confidence-weighted averaging fusion strategy resulted in the best overall performance, achieving 96.7% accuracy and 98.5% AUC, confirming the added value of the addition of the multimodal fusion, as the last layer of the suggested pipeline, over standalone detection components. The framework's robustness was validated across diverse threat types, including zero-day exploits and botnet intrusions. Synthetic data generated through the selected ZDGAN and DCGAN models significantly improved generalization and resilience to rare or unseen threats. Moreover, the architecture's ability to handle heterogeneous data streams and perform input-type recognition dynamically highlights its adaptability to real-world deployment environments. The extensive evaluation highlighted its scalability, modularity, and extensibility, making it suitable for cybersecurity infrastructures such as intrusion detection systems, malware analysis sandboxes, and endpoint security platforms. The proposed multimodal framework has been designed with deployment adaptability in mind. Its modular architecture enables seamless integration into real-world IDS environments such as Security Information and Event Management (SIEM) or Extended Detection and Response (XDR) platforms. Each processing branch can function independently or jointly, depending on available data inputs, allowing the system to adapt to partial or multimodal threat contexts. Modern SIEM/XDR systems increasingly support the integration of external AI/ML modules, either through dedicated APIs, plugin environments, or data enrichment pipelines, making it feasible to deploy our threat detection branches as external microservices or real-time classification components. Additionally, the inclusion of an input-type recognition layer and support for configurable logging and fusion strategies \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Accuracy** & **F1-Score** & **Precision** \\ \hline Full Model & **0.967** & **0.966** & **0.969** \\ No GAN-based Augmentation & 0.903 & 0.927 & 0.931 \\ No Transfer Learning & 0.923 & 0.939 & 0.944 \\ No Fusion Layer & 0.946 & 0.944 & 0.948 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Summarization of Ablation Experiment Results. makes the pipeline suitable for deployment within malware sandboxing tools, enterprise detection networks, or endpoint monitoring environments. These features, combined with its low computational footprint and reliance on standard deep learning components, ensure that the proposed system is not only theoretically sound but also practically feasible. Despite the strengths of the proposed framework, some limitations should be acknowledged. The experiments were conducted using public benchmark datasets, which may not fully reflect the complexity and variability of real-world network traffic. Furthermore, while efforts were made to validate the synthetic data's quality and diversity, its real-world representativeness cannot be guaranteed. The framework also assumes clean input labels and no adversarial manipulation, which might not be held in dynamic or hostile environments. Future work could address these limitations through deployment in live traffic settings, adversarial robustness evaluation, and integration of additional data modalities such as behavioral or textual data.