Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach

Autor: Hoang Duy Trinh, Engin Zeydan, Lorenza Giupponi, Paolo Dini

Publikováno v: IEEE Access, Vol 7, Pp 152187-152201 (2019)

Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, publ

Externí odkaz: https://doaj.org/article/2332fa181de84a209e8fa0af11cb3a7c

Mobile Traffic Classification through Physical Control Channel Fingerprinting: a Deep Learning Approach

Autor: Paolo Dini, Angel Fernandez Gambin, Michele Rossi, Lorenza Giupponi, Hoang Duy Trinh

Publikováno v: IEEE Transactions on Network and Service Management
r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname
r-CTTC: Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime , from the raw physical control channel of an operative m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ef5ae021ba39ee54103d406e06ecb34
http://hdl.handle.net/11577/3367824

Proactive Wake-up Scheduler based on Recurrent Neural Networks

Autor: Mario Costa, Mikko Valkama, Hoang Duy Trinh, Sandra Lagen, Paolo Dini, Soheil Rostami

Publikováno v: ICC

Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to sp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f9b1abc382f897baaeb577eafefdc6c
https://trepo.tuni.fi/handle/10024/129930

Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach

Autor: Paolo Dini, Lorenza Giupponi, Engin Zeydan, Hoang Duy Trinh

Publikováno v: IEEE Access, Vol 7, Pp 152187-152201 (2019)
IEEE Access
r-CTTC: Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname

Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, publ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b4e7b08024e2121a8be5c3dde0efce3
https://ieeexplore.ieee.org/document/8871152/

Urban Anomaly Detection by Processing Mobile Traffic Traces with LSTM Neural Networks

Autor: Lorenza Giupponi, Hoang Duy Trinh, Paolo Dini

Publikováno v: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
SECON

Detecting urban anomalies is of upmost importance for public order management, since they can pose serious risks to public safety if not timely handled. However, monitoring large metropolitan areas requires complex systems that can potentially lead t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22c8d6a899dee5a2a1ed2a4b0c8fe587
https://zenodo.org/record/3626892