## 4 Limitations and Future Research Directions
This approach could leverage adaptive thresholds for anomaly detection, dynamically adjusting based on the latest network patterns. Additionally, employing reinforcement learning in conjunction with our proposed methods could optimize model retraining schedules, ensuring adaptability without sacrificing efficiency. * Complexity and Interpretability: Deep learning models are often seen as 'black boxes', offering limited interpretability regarding their decision-making processes. This can be a drawback in network security contexts where understanding the rationale behind anomaly detection is crucial for trust and further action. Future research could integrate explainability techniques, such as SHAP (SHapley Additive exPlanations) [51] or attention mechanisms [24], into the proposed models to provide insights into how specific latent space features contribute to anomaly classification. The comparison in our study indicates that better-organized latent spaces inherently aid in model interpretability, which could be a valuable avenue for further work. * Environmental Specific Challenges: Large-scale, dynamic environments like enterprise networks or cloud infrastructures present unique challenges. Future research could adapt the proposed modifications to hybrid models, combining rule-based detection with machine learning to enhance performance in these settings. Furthermore, testing our approach on time-series data in real-world environments, such as IoT systems, could validate its scalability and applicability in diverse use cases. Incorporating domain-specific preprocessing techniques could further enhance model generalization across varied network environments. Additionally, adaptive algorithms, such as the Generalized Finite Element Method (GFEM) for multiscale environments [52], may provide innovative frameworks for refining detection in complex systems. * Integration with Big Data Pipelines: Our framework leverages tools like Apache Spark and Elasticsearch for efficient data handling. Future research could explore integrating our modified models into real-time analytics pipelines, optimizing for high-throughput scenarios. Applying streaming frameworks, such as Flink, could enable near-instantaneous anomaly detection and improve system response times in critical environments. * Extension to Other Domains: Although our study focused on network anomaly detection, the modified cost functions and latent space manipulations could be applied to other domains with complex, high-dimensional data, such as healthcare, finance, and industrial systems. For example, anomaly detection in medical imaging or financial fraud could benefit from our approach's enhanced sensitivity and specificity. Future research could validate the versatility of our methods across these domains, tailoring the architecture and preprocessing to domain-specific characteristics. By addressing these challenges and exploring the outlined research directions, we aim to build on the foundation established in this study. The proposed modifications to cost functions, latent space design, and model training strategies demonstrate significant potential to advance the field of anomaly detection. Continued refinement and application of these innovations will enable the development of robust, efficient, and interpretable solutions tailored to the evolving demands of modern systems.