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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network
ترجمه فارسی عنوان مقاله:
تجزیه و تحلیل داده بزرگ برای تجزیه و تحلیل کاربری فعالیت و تشخیص ناهنجاری کاربر در شبکه های بی سیم سیار
منبع:
IEEE -This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2017.2650206, IEEE Transactions on Industrial Informatics
نویسنده:
Md Salik Parwez,Danda B. Rawat, Moses Garuba
چکیده انگلیسی:
The next generation wireless networks are expected to
operate in fully automated fashion to meet the burgeoning
capacity demand and to serve users with superior quality of
experience. Mobile wireless networks can leverage spatiotemporal information about user and network condition to
embed the system with end-to-end visibility and intelligence. Big
data analytics has emerged as a promising approach to unearth
meaningful insights and to build artificially intelligent models
with assistance of machine learning tools. Utilizing
aforementioned tools and techniques, this paper contributes in
two ways. First, we utilize mobile network data (big data) – call
detail record (CDR) – to analyze anomalous behavior of mobile
wireless network. For anomaly detection purposes, we use
unsupervised clustering techniques namely k-means clustering
and hierarchical clustering. We compare the detected anomalies
with ground truth information to verify their correctness. From
the comparative analysis, we observe that when the network
experiences abruptly high (unusual) traffic demand at any
location and time, it identifies that as anomaly. This helps in
identifying regions of interest (RoI) in the network for special
action such as resource allocation, fault avoidance solution etc.
Second, we train a neural-network based prediction model with
anomalous and anomaly-free data to highlight the effect of
anomalies in data while training/building intelligent models. In
this phase, we transform our anomalous data to anomaly-free
and we observe that the error in prediction while training the
model with anomaly-free data has largely decreased as compared
to the case when the model was trained with anomalous data.
Index Terms: Next generation wireless networks | 5G | Anomaly detection | call detail record | machine learning | network analytics | network behavior analysis | wireless cellular network
قیمت: رایگان
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