## 2 Related Deep Learning Algorithms
### Summary
over-complication, where the model's sophistication does not necessarily translate to better performance in practical anomaly detection tasks. The complexity of these models can lead to increased computational requirements and slower operational speeds, which the incremental gains in accuracy or detection capabilities may not justify. Therefore, these advanced methods should be case-specific and thoroughly evaluated against the nature of the data and the specific requirements of the anomaly detection task. Despite the apparent advantages of deep learning models in anomaly detection, their implementation is not without challenges. Data Imbalance: Typically, datasets contain a vast majority of \"normal\" data points compared to a relatively small number of anomalies. This imbalance makes it difficult for the neural network to learn the characteristics of anomalies effectively, as the overwhelming presence of normal data can lead to a bias toward classifying most inputs as normal. Defining the Hidden Space Representation: The effectiveness of an autoencoder in anomaly detection hinges on its ability to learn a comprehensive representation of the data in its hidden space. The challenge lies in designing an autoencoder that can capture the essence of normal data so that anomalies are distinguishable when they fail to conform to this learned representation. This requires a careful balance between making the hidden space too general, which might overlook subtle anomalies, and making it too specific, which could falsely flag normal variations as anomalies. Selection of Loss Functions and Thresholds: The choice of loss function can significantly impact anomaly detection performance. The loss function must be sensitive enough to highlight deviations in the reconstruction of data points yet robust enough to ignore insignificant variations. Additionally, setting the appropriate threshold for distinguishing between normal and anomalous data is crucial and challenging, as it often requires domain knowledge and iterative experimentation. Complexity of Anomalies: Anomalies can manifest in various forms and might not always be straightforward deviations from the norm. Some anomalies could be contextual, where their anomalous nature is only apparent in certain situations or combinations of features. This complexity necessitates a neural network architecture that can understand and model the intricacies of normal behavior and its exceptions. Evolving Data Patterns: In many applications, the definition of normal behavior can change over time. This evolution poses a challenge for anomaly detection systems, which must adapt to new patterns of normal behavior without losing the ability to detect true anomalies. The detection difficulty of network anomalies lies in the subtlety and diversity of such irregularities, which often blend in with standard traffic patterns, making them hard to identify without sophisticated analysis. Furthermore, for example, the dynamic nature of network traffic, combined with the continuous evolution of cyber threats, adds to the complexity of establishing a reliable detection system. Moreover, the high dimensionality of data and the need for real-time detection introduce additional computational burdens. These challenges underscore the need for ongoing research and innovation to refine deep learning methods for more accurate and efficient anomaly detection. Internal Analysis of the Impact of the Autoencoder Network Structure and Training Method on Anomaly Detection  
For analysis and description of practical usage, this article introduces a deep learning (DL) framework for detecting network traffic anomalies. Our research presents a DL-based solution that addresses the inherent limitations of traditional anomaly detection systems. By innovatively integrating multi-dimensional data, our approach simplifies the complexitiesassociated with individual anomaly modeling and enhances the aggregation process of anomaly detection results.