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When smartphones become the enemy: unveiling mobile apps anomalies through clustering techniques.

, , and . ATC@MobiCom, page 19-24. ACM, (2016)

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On the Role of Flows and Sessions in Internet Traffic Modeling: An Explorative Toy-Model., , , , , and . GLOBECOM, page 1-8. IEEE, (2009)Device-Specific Traffic Characterization for Root Cause Analysis in Cellular Networks., , and . TMA, volume 9053 of Lecture Notes in Computer Science, page 64-78. Springer, (2015)A Survey on Big Data for Network Traffic Monitoring and Analysis., , , , and . IEEE Trans. Network and Service Management, 16 (3): 800-813 (2019)COMINDIS - COllaborative Monitoring with MINimum DISclosure (Poster)., , and . TMA, volume 6613 of Lecture Notes in Computer Science, page 189-192. Springer, (2011)YouTube in the move: Understanding the performance of YouTube in cellular networks., , , and . Wireless Days, page 1-6. IEEE, (2014)Detecting and diagnosing anomalies in cellular networks using Random Neural Networks., , , and . IWCMC, page 351-356. IEEE, (2016)Network security and anomaly detection with Big-DAMA, a big data analytics framework., , , , and . CloudNet, page 16-22. IEEE, (2017)YouTube all around: Characterizing YouTube from mobile and fixed-line network vantage points., , , , , and . EuCNC, page 1-5. IEEE, (2014)Machine-Learning Based Approaches for Anomaly Detection and Classification in Cellular Networks., , and . TMA, IFIP, (2016)Modeling the AIADD paradigm in networks with variable delays., , , , , , and . CoNEXT, page 16. ACM, (2006)