XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection
arXiv:2502.09194v1 Announce Type: cross 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. Howe
The increasing complexity of O-RAN architectures and the push for disaggregated, multi-vendor components necessitate advanced AI solutions for network management and anomaly detection.
This development signifies the growing integration of sophisticated AI, specifically generative AI and explainable AI, into critical infrastructure like telecommunications, enhancing operational resilience and security.
Traffic anomaly detection in O-RAN becomes more robust and transparent through explainable deep learning, allowing for better management and quicker resolution of network issues.
- · Telecommunication operators
- · O-RAN vendors
- · AI/ML solution providers
- · Network security firms
- · Traditional anomaly detection methods
- · Networks without advanced monitoring
Improved stability and efficiency of open radio access networks through AI-driven anomaly detection.
Reduced operational costs and enhanced security posture for O-RAN deployments, accelerating their adoption.
The development of more autonomous and self-optimizing telecommunication networks, potentially reducing human intervention significantly.
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Read at arXiv cs.AI