###### Abstract  
This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches. a 2025 15 286 17 December 2024 30 December 2024 3  
Similarly, in manufacturing, ensuring product quality involves detecting subtle anomalies in sensor readings or production line data, requiring systems that can operate in real time and minimize downtime [10; 11; 12; 13]. The network security domain often involves evolving threats and high-dimensional data, where adaptability to new patterns and low latency in detection are critical. Conversely, in marketing and social media analytics, the goal is to identify behavioral anomalies among users or trends, where the system must balance precision with the capacity to analyze large-scale data [14; 15; 16; 17]. These diverse demands highlight the importance of tailoring anomaly detection systems to the specific requirements of each domain, which often influences the choice of models, training strategies, and evaluation metrics. Despite the significant advancements in anomaly detection, current methods face challenges in achieving robust performance in complex, multi-dimensional data environments. Anomalies are often rare and context-dependent, making them difficult to identify using traditional approaches. Deep learning-based methods, such as autoencoders, have emerged as powerful tools for addressing these challenges. These methods excel in modeling non-linear dependencies and handling high-dimensional data, enabling them to detect subtle deviations from normal behavior. Approaches based on deep learning, when applied to detect anomalies, offer several advantages. Firstly, those approaches are designed to work with multi-dimensional data. This facilitates the integration of information from many sources and eliminates challenges associated with individual modeling of anomalies for each variable and with aggregation of results. The deep learning approaches are also well-adapted to joint modeling of interactions between multiple variables concerning a given task; moreover, the deep learning models require minimal tuning to achieve good results and enable the determination of general hyperparameters (the number of layers, units per layer, etc.). Another advantage is their efficiency. The deep learning methods offer the ability to model complex, non-linear dependencies within the data and use them to detect anomalies. The efficiency of the deep learning models may also scale with the availability of proper training data, making them appropriate for problems rich in data. Deep learning models can adapt to evolving network threats, particularly those trained semi-supervised [1]. They can detect anomalies based on learned patterns and deviations, even when the exact nature of the threat is new or unknown.