Advanced Anomaly Detection in 5G Wireless Systems: A Hybrid Learning Paradigm Utilizing the AWID3 Dataset
Keywords:
5G wireless systems, anomaly detection, hybrid learning, AWID3 datasetAbstract
The rapid evolution of 5G wireless networks has introduced unprecedented capabilities alongside heightened security challenges, particularly in detecting sophisticated network anomalies. This paper proposes an advanced anomaly detection framework that leverages a hybrid learning paradigm combining supervised and unsupervised techniques to enhance detection accuracy and adaptability in 5G environments. Utilizing the AWID3 dataset as a comprehensive benchmark, the study integrates deep learning-based feature extraction with ensemble classifiers to identify known and unknown attack patterns. Extensive experiments demonstrate that the hybrid approach outperforms conventional single-method models in terms of detection rate, false positive reduction, and computational efficiency. The findings highlight the critical role of hybrid learning architectures and rich wireless intrusion datasets in fortifying next-generation communication infrastructures against evolving cyber threats.
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Copyright (c) 2025 Dr. Aisha M. Yusuf
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