XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

TL;DR

This paper introduces XAInomaly, an explainable deep autoencoder framework designed for anomaly detection in O-RAN networks, emphasizing interpretability, scalability, and effective network management.

Contribution

It presents a novel semi-supervised deep contractive autoencoder with an explainable AI technique tailored for O-RAN anomaly detection, addressing interpretability and scalability challenges.

Findings

Effective detection of network anomalies in O-RAN.

Enhanced interpretability through fastshap-C explainability.

Robust representation learning of normal network behavior.

Abstract

Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance. In the realm of open radio access networks (O-RAN), characterized by their disaggregated architecture and heterogeneous components from multiple vendors, the deployment of generative models offers significant advantages for network management such as traffic analysis, traffic forecasting and anomaly detection. However, the complex and dynamic nature of O-RAN introduces challenges that necessitate not only accurate detection mechanisms but also reduced complexity, scalability, and most importantly interpretability to facilitate effective network management. In this study, we introduce the XAInomaly framework, an explainable and interpretable Semi-supervised (SS) Deep Contractive Autoencoder (DeepCAE) design for anomaly detection in O-RAN. Our approach leverages the generative modeling capabilities of our SS-DeepCAE model to learn compressed, robust representations of normal network behavior, which captures essential features, enabling the identification of deviations indicative of anomalies. To address the black-box nature of deep learning models, we propose reactive Explainable AI (XAI) technique called fastshap-C.