Challenging Entropy-based Anomaly Detection and Diagnosis in Cellular Networks

被引：2
|
作者：
Fiadino, P. [1 ]
D'Alconzo, A. [1 ]
Schiavone, M. [1 ]
Casas, P. [1 ]
机构：
[1] FTW Vienna, Vienna, Austria
来源：
ACM SIGCOMM COMPUTER COMMUNICATION REVIEW | 2015年 / 45卷 / 04期
关键词：
D O I：
10.1145/2829988.2790011
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we challenge the applicability of entropy-based approaches for detecting and diagnosis network traffic anomalies, and claim that full statistics (i.e., empirical probability distributions) should be applied to improve the change detection capabilities. We support our claim by detecting and diagnosing large-scale traffic anomalies in a real cellular network, caused by specific OTT (Over The Top) services and smartphone devices. Our results clearly suggest that anomaly detection and diagnosis based on entropy analysis is prone to errors and misses typical characteristics of traffic anomalies, particularly in the studied scenario.(1)
引用
收藏
页码：87 / 88
页数：2
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