Open AccessAnomaly detection for cellular networks using big data analytics
Author(s) -
Li Bing,
Zhao Shengjie,
Zhang Rongqing,
Shi Qingjiang,
Yang Kai
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0765
Subject(s) - anomaly detection , cellular network , big data , computer science , mobile broadband , anomaly (physics) , data science , data mining , computer network , telecommunications , wireless , physics , condensed matter physics
Broadband connectivity and mobile technology have been widely applied in the world. With these advanced technologies, the proliferation of smart devices and their applications by accessing mobile internet have come up with a giant leap forward, leading to the ever‐increasing scale and complexity of cellular networks. This presents imminent challenges to anomaly detection in cellular networks. In this study, the authors discuss challenges and current literature of anomaly detection for cellular networks to embrace the ‘big data’ era. First, they review the state‐of‐the‐art techniques in the area of anomaly detection in cellular networks. Then, the challenges are pinpointed for anomaly detection due to the cellular network big data. Finally, they introduce a big data analytic‐based anomaly detection method for cellular networks.