Autoencoders for Anomaly Detection: A Comprehensive Architectural Review, Comparative Insights, and Practical Guidance

Anomaly detection is crucial in modern data-driven applications such as healthcare, industrial monitoring, and cybersecurity, where traditional methods often face challenges with complex, high-dimensional data. Autoencoders, a form of unsupervised neural network that learns compact data representations, have become valuable tools for identifying anomalies through reconstruction errors. This paper reviews a range of autoencoder architectures used for anomaly detection, carefully evaluating their advantages, limitations, and applicability across different types of data and anomaly patterns. The discussion begins with basic models including vanilla, sparse, and denoising autoencoders, before moving on to more advanced forms such as variational, adversarial, recurrent, and attention-based variants. Key design considerations like the depth of encoder and decoder networks, the structure of latent spaces, and regularization techniques are examined to support effective model development. Real-world applications, including fraud detection and IoT fault diagnosis, are highlighted, along with challenges related to computational demands and robustness in the face of adversarial interference and streaming data. Standard performance metrics like precision, F1-score, and AUC-ROC are covered, together with reconstruction error measures such as mean squared error and mean absolute error, to evaluate detection accuracy. Finally, the review identifies ongoing challenges, including the selection of decision thresholds, interpretability of results, and risks of overfitting, and proposes future research directions focused on hybrid models, self-supervised learning, and improved explainability to enhance the deployment of autoencoder-based anomaly detection systems.