Traffic Anomaly Detection Using Deep Semi-Supervised Learning at the Mobile Edge
Por:
Pelati, A, Meo, M, Dini, P
Publicada:
1 ago 2022
Resumen:
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.
Filiaciones:
Pelati, A:
Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy
Politecn Torino, Dept Elect & Telecommun, Turin, Italy
Meo, M:
Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy
Politecn Torino, Dept Elect & Telecommun, Turin, Italy
Dini, P:
Politecnico di Torino, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Torino, Italy
Politecn Torino, Ctr Tecnol Telecomunicac Catalunya CTTC CERCA, Turin, Italy
Green Published, All Open Access; Green Open Access
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