A data plane approach for detecting control plane anomalies in mobile networks
File(s)
Accepted version
Author(s)
Abdelrahman, OH
Gelenbe, E
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.
Editor(s)
Mandler, B
MarquezBarja, J
Campista, MEM
Caganova, D
Chaouchi, H
Zeadally, S
Badra, M
Giordano, S
Fazio, M
Somov, A
Vieriu, RL
Date Issued
2016-10-18
Date Acceptance
2015-10-01
Citation
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2016, 169, pp.210-221
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
Commission of the European Communities
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000398616500019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
317888 - NEMESYS
Source
2nd International Summit on Internet of Things - IoT Infrastructures( IoT 360)
Subjects
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
Coverage Spatial
Rome, Italy