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

Researchers introduce XAInomaly, an explainable AI framework using a Semi-supervised Deep Contractive Autoencoder for detecting anomalies in Open RAN (O-RAN) networks. The system addresses the critical need for interpretable machine learning in complex wireless infrastructure by combining generative modeling with explainability techniques to identify network traffic deviations while maintaining transparency in decision-making.

The emergence of Open RAN architectures represents a significant shift in wireless network infrastructure, moving away from proprietary systems toward disaggregated, vendor-neutral designs. This transition introduces substantial operational challenges, particularly in monitoring heterogeneous network components from multiple vendors. XAInomaly directly addresses these challenges by proposing an interpretable machine learning solution specifically engineered for O-RAN environments.

The framework's significance lies in bridging two critical requirements that often conflict in AI deployment: accuracy and explainability. Traditional deep learning anomaly detection systems excel at identifying network irregularities but operate as black boxes, making it difficult for network operators to understand why systems flagged specific traffic patterns as anomalous. By integrating a Semi-supervised Deep Contractive Autoencoder with fastshap-C explainability techniques, XAInomaly enables operators to understand the reasoning behind anomaly detection decisions.

For telecommunications operators managing complex O-RAN deployments, this framework offers practical advantages beyond detection accuracy. The reduced complexity and improved scalability characteristics make the solution viable for real-world deployment across distributed networks. The semi-supervised approach proves particularly valuable since collecting fully labeled anomalous network data remains challenging in operational environments.

Looking forward, interpretable AI solutions like XAInomaly will become essential as telecommunications networks grow increasingly complex and AI-dependent. The framework's focus on explainability sets precedent for other critical infrastructure applications where understanding algorithmic decisions carries regulatory and operational importance. Continued development in this direction could influence how network management evolves across the industry.

• →XAInomaly introduces an interpretable autoencoder framework specifically designed for anomaly detection in disaggregated O-RAN architectures.
• →The Semi-supervised Deep Contractive Autoencoder addresses the black-box problem by integrating explainable AI techniques (fastshap-C) into network anomaly detection.
• →The framework reduces computational complexity while maintaining scalability, making it practical for real-world deployment in heterogeneous network environments.
• →Semi-supervised learning capabilities enable effective anomaly detection despite limited labeled data, a common challenge in operational telecom networks.
• →Explainable AI in critical infrastructure supports regulatory compliance and operator confidence in automated network management decisions.