A data plane approach for detecting control plane anomalies in mobile networks

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Title: A data plane approach for detecting control plane anomalies in mobile networks
Authors: Abdelrahman, OH
Gelenbe, E
Item Type: Conference Paper
Abstract: This paper proposes an anomaly detection framework that utilizes key performance indicators (KPIs) and traffic measurements to identify in real-time misbehaving mobile devices that contribute to signaling overloads in cellular networks. The detection algorithm selects the devices to monitor and adjusts its own parameters based on KPIs, then computes various features from Internet traffic that capture both sudden and long term changes in behavior, and finally combines the information gathered from the individual features using a random neural network in order to detect anomalous users. The approach is validated using data generated by a detailed mobile network simulator.
Editors: Mandler, B
MarquezBarja, J
Campista, MEM
Caganova, D
Chaouchi, H
Zeadally, S
Badra, M
Giordano, S
Fazio, M
Somov, A
Vieriu, RL
Issue Date: 18-Oct-2016
Date of Acceptance: 1-Oct-2015
URI: http://hdl.handle.net/10044/1/51303
DOI: https://dx.doi.org/10.1007/978-3-319-47063-4_19
ISBN: 978-3-319-47062-7
ISSN: 1867-8211
Publisher: Springer Verlag (Germany)
Start Page: 210
End Page: 221
Journal / Book Title: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume: 169
Copyright Statement: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 317888 - NEMESYS
Conference Name: 2nd International Summit on Internet of Things - IoT Infrastructures( IoT 360)
Keywords: Science & Technology
Technology
Computer Science, Information Systems
Computer Science, Software Engineering
Computer Science, Theory & Methods
Telecommunications
Computer Science
Mobile security
Random neural network
M2M
IoT
Signaling overload
Radio resource control
Key performance indicators
RANDOM NEURAL-NETWORK
Publication Status: Published
Start Date: 2015-10-27
Finish Date: 2015-10-29
Conference Place: Rome, Italy
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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