Anomaly Detection and Localization in NFV Systems by Utilizing Masked-Autoencoder and XAI

The integration of Network Functions Virtualization (NFV) systems into mobile edge and core networks has heightened the need for effective anomaly detection and localization methods. The complexity of NFV demands robust mechanisms for network resilience, security, and performance. Machine Learning approaches have demonstrated promising solutions in crafting adaptive and efficient mechanisms for detecting and localizing potential anomalies within NFV systems. Particularly, Unsupervised Learning (UL) methods have garnered significant attention for their potential to detect anomalies without the need for labeled data. However, UL methods are susceptible to even minor levels of anomalous samples in the training data, termed contamination, which can severely compromise their performance. This paper proposes a novel approach using the Noisy-Student technique for anomaly detection. It addresses data contamination by combining a density-estimation teacher model for pseudo-labeling with a weakly-supervised student model based on a Masked Autoencoder trained on the pseudo-labeled data. For anomaly localization, we introduce a heuristic tailored for our anomaly detection model and two Explainable Artificial Intelligence (XAI)-based approaches applicable to any detection model. Extensive experiments on three NFV datasets demonstrate superior performance, with up to a 20% improvement in anomaly detection and up to a 22% improvement in localization, in terms of F1-score.