Fortunately, we at LibertyAces track these anomalies and get alerted whenever a base station exceeds the thresholds. This particular case was resolved in a matter of a couple of hours.
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Several AI techniques are employed in detecting anomalies associated with rogue base stations. Machine learning models, such as supervised and unsupervised learning, are trained on
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The evolution of anomaly detection in telecom infrastructure reflects technological advancements as well as the increasing complexity of telecommunications networks. Since the
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In this paper, we design and build an anomaly detection system to detect such active injection threats to the victim user equipment''s availability. Our detection scheme implements
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To enable mobile networks to operate efficiently, troubleshooting the Baseband Unit, or BBU, as it is more commonly known, is required. BBUs are critical to interface with the core network
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This work presents an anomaly detection framework using Digital Twin (DT) technology to simulate and monitor dynamic radio environments.
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Radio access networks (RAN) produce large amounts of operational data, making manual performance monitoring difficult and inefficient. This study compares three automated anomaly detection methods:
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Due to the immense volume of the data, automatic solutions are needed to be able to detect such anomalies. This thesis explores the design of an automatic anomaly detection method
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In this paper, it is examined how model-driven approach can be adopted to automatize machine learning tasks aiming mobile network data analysis. The framework is evaluated on classification task for
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