# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 7 Conclusions  
This study presents the MVT as an innovative and robust proposal for zero-day threat detection in cloud computing environments, integrating advanced deep learning techniques and a visual representation of executable files. The results obtained, with an average accuracy ranging between 70% and 80%, demonstrate the model's capability to effectively detect unknown threats, surpassing the performance of traditional models in highly challenging and class-imbalanced scenarios. However, significant limitations were identified, particularly regarding the classification of benign files, high computational cost, and the lack of explainability inherent to transformer-based architectures. These findings highlight the need for continued research into architectural optimization strategies, data balancing through targeted augmentation techniques for benign files, and the integration of explainable artificial intelligence approaches to enable understanding and validation of the model's decisions in operational contexts. Additionally, developing multiclass classification capabilities was identified as a future research line, improving the model's practical utility and granularity within cloud security ecosystems. In summary, MVT represents a relevant contribution toward adaptive and proactive detection systems aligned with the emerging cybersecurity challenges in distributed, dynamic, and heterogeneous environments. Nonetheless, to achieve its industrial and operational adoption, it is imperative to address the identified gaps through future research focused on computational efficiency, interpretability, and validation in real-world and adversarial scenarios. With these improvements, MVT could become an essential component within modern cybersecurity frameworks, strengthening the resilience of critical infrastructures against zero-day attacks. Based on the findings of this study, several key research directions have been identified to enhance the effectiveness and applicability of the MVT model in real-world zero-day threat detection scenarios in cloud environments:  
* Architectural optimization and computational efficiency: Research should focus on optimizing transformer-based architectures, including pruning, quantization, and the development of lightweight variants that significantly reduce computational requirements, thus enabling model integration into hybrid cloud, edge, and resource-constrained systems. * Improving class balance and benign file representation: Advanced data augmentation methods specifically designed to enhance the diversity and representativeness of benign files are recommended. Additionally, the use of synthetic generators based on Generative Adversarial Networks (GANs) could provide realistic samples to help mitigate the model's observed bias. * Multiclass classification and malware family detection: Expanding the current binary classification approach to a multiclass scheme will allow the identification of not only the presence of malware but also differentiation among families or types of threats, offering greater operational value and facilitating early response to cyber incidents. * Integration of explainable AI (XAI) techniques: Incorporating XAI methodologies that enable visualization of the model's decisions, identification of relevant patterns in visual representations, and facilitation of auditing and validation processes by human analysts will improve the trust, transparency, and regulatory acceptance of MVT-based systems. * Evaluation in operational environments and adversarial robustness: Critical validation of the model in dynamic and real-world cloud computing environments, subjected to variable traffic, latency, and emerging threat conditions, is recommended. Additionally, it is essential to investigate the model's robustness against adversarial attacks designed to evade deep learning-based detection systems.