# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 6 Discussion  
The experimental results reveal that the MVT model consistently outperforms traditional methods such as Decision Trees (DT) and Convolutional Neural Networks (CNNs) when identifying zero-day malware. This superior performance is attributed to the model's transformer-based architecture, which is particularly effective at capturing complex patterns and generalizing to unseen samples [12; 14; 34]. Despite the application of the SMOTE technique to address class imbalance, the model still demonstrates a tendency to favor malware detection over the classification of benign files (Class 0). This lower performance on benign samples suggests that the training process may have introduced a bias, revealing a persistent challenge in handling imbalanced data. Consequently, it becomes essential to investigate more advanced data balancing strategies, including adaptive learning methods and enhanced data augmentation techniques, specifically designed to improve the representation and learning of minority classes [5; 13; 23; 41].  
Another challenge is the model's computational efficiency. Transformer-based architectures are known for their high resource demands, which can complicate real-time deployment in environments with limited processing capacity. Exploring lightweight alternatives, such as hybrid CNN transformer models or optimized versions like EfficientNet, could help reduce overhead while maintaining accuracy [12; 15; 42].  
Interpretability also remains a concern. Due to the complexity of attention mechanisms, it is often difficult to understand why a particular input triggers a specific prediction. Explainable AI (XAI) tools such as attention visualization or feature attribution can provide insights into model behavior and help foster trust in operational settings [13; 33].  
Although the MVT model has shown high accuracy under controlled conditions, its resilience against adversarial and evasive attacks remains unassessed, a significant limitation considering that real-world threat environments often involve adaptive attackers aiming to evade detection. Transformer-based architectures have demonstrated particular susceptibility to such threats. [PERSON] et al. [43] reported substantial accuracy degradation when adversarial perturbations were applied to transformer models in malware detection tasks. Similarly, [PERSON] et al. [44] revealed that adversarial samples could transfer between models with over 25% effectiveness, highlighting the risk of model-agnostic evasion strategies.  
In response, recent research has focused on developing defense mechanisms tailored for deep and attention-based architectures. [PERSON] et al. [45] provide an updated taxonomy of attack and mitigation techniques relevant to these models. Notably, the PAD framework proposed by [PERSON] et al. [46] achieves robustness against 27 different evasion methods while maintaining an operational accuracy of 83%.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Metric** & **Mean (ND)** & **Mean (WD)** & \(p\)**-Value (Wilcoxon)** & **Effect Size \(r\)** \\ \hline Precision & 0.8938 & 0.9033 & 0.021 & 0.44 \\ F1-Score & 0.8945 & 0.9037 & 0.017 & 0.47 \\ \hline \end{tabular}
\end{table}
Table 4: Statistical comparison between MVT configurations (without and with dropout (ND, WD).  
These findings emphasize the urgent need to evaluate the MVT model under adversarial conditions to ensure its reliability and applicability in real-world cybersecurity deployments. While traditional models may achieve slightly higher accuracy in controlled benchmarks, their capacity to detect novel or obfuscated threats remains limited. In contrast, the MVT model has demonstrated superior generalization in previously unseen scenarios, reaffirming the potential of transformer-based architectures for malware classification. Nonetheless, future work should focus on enhancing the model's robustness against adversarial attacks, optimizing its computational efficiency, and improving its interpretability to ensure its suitability for operational environments.