Paper Info
Title
When smartphones become the enemy: unveiling mobile apps anomalies through clustering techniques.
Abstract
The ever-increasing number of mobile devices connected to cellular networks is heavily modifying the traffic observed in these networks. The traffic volumes and patterns generated by smartphones pose novel challenges to cellular network operators. One of these challenges relates to the automatic detection and diagnosis of unforeseen network traffic anomalies caused by specific devices and apps. Synchronized apps generating flashcrowds, device-specific traffic misbehaviors impacting network performance and end-users Quality of Experience (QoE), and other similar anomalies need to be rapidly detected and diagnosed. In this paper we characterize a new type of anomalies impacting cellular networks, caused by the multiple, constantly-connected apps running in smartphones and other end-user devices. We additionally devise a novel detection and classification technique based on semi-supervised Machine Learning (ML) algorithms to automatically detect and diagnose anomalies of this class with minimal training, and compare its performance to that achieved by other well-known supervised learning classifiers. The proposed solution is evaluated using synthetically generated data from an operational cellular ISP, drawn from real traffic statistics to resemble both the real cellular network traffic and the characterized type of anomalies.
Year
DOI
Venue
2016
10.1145/2980055.2980058
ATC@MobiCom
Field
DocType
Citations 
Computer science,Computer network,Supervised learning,Mobile device,Quality of experience,Cellular network,Adversary,Cluster analysis,Mobile apps,Network performance
Conference
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
Pedro Casas1697.09
Pierdomenico Fiadino211911.16
Alessandro D'Alconzo333026.01