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Title	Traffic Anomaly Detection Using Deep Semi-Supervised Learning At The Mobile Edge
ID_Doc	58527
Authors	Pelati A.; Meo M.; Dini P.
Year	2022
Published	IEEE Transactions on Vehicular Technology, 71, 8
DOI	http://dx.doi.org/10.1109/TVT.2022.3174735
Abstract	In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users' behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause. © 1967-2012 IEEE.
Author Keywords	Anomaly detection; distributed learning; edge computing; mobile networks; remote sensing; smart cities; traffic modeling

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