A Distributed Trustable AI Engine for Anomaly Detection in 6G Networks: Architecture, Use Cases and Performance Evaluation

The evolution towards sixth-generation (6G) networks requires new architecture enhancements to support the broad device ecosystem, comprising users, machines, autonomous vehicle and Internet-of-Things devices. Moreover, high heterogeneity in the desired Quality-of-Service (QoS) is expected, as 6G networks will offer extremely low-latency and high-throughput services and error-free communication. This complex environment raises significant challenges in resource management, while adhering to security and privacy constraints, due to the plethora of data generation endpoints. In this work, we present an artificial intelligence/machine learning (AI/ML)-aided distributed trustable engine (DTE), collecting data from diverse sources of the 6G infrastructure and employing AI/ML modules for anomaly detection against diverse threat types. Moreover, we present the DTE architecture and its components, providing data management, AI/ML model training and classification capabilities for anomaly detection. To promote privacy-aware networking, a federated learning (FL) framework to extend the DTE is discussed. Then, the anomaly detection capabilities of the AI/ML-aided DTE are presented in detail together with the ML model training process, considering various ML models. For this purpose, we use two open datasets, representing attack scenarios in the core and the edge parts of the network. It is shown that the AI/ML-aided DTE can efficiently train ML models with reduced dimensionality and deploy them in diverse cybersecurity scenarios to improve anomaly detection in 6G networks.