NDLI: Tool support for the evaluation of anomaly traffic classification for network resilience

Content Provider	IEEE Xplore Digital Library
Author	Santos da Silva, Anderson Wickboldt, Juliano Araujo Schaeffer-Filho, Alberto Marnerides, Angelos K. Mauthe, Andreas
Copyright Year	2015
Description	Author affiliation: School of Computing and Communications, Lancaster University, United Kingdom (Mauthe, Andreas) \|\| School of Computing & Mathematical Sciences, Liverpool John Moores University, United Kingdom (Marnerides, Angelos K.) \|\| Institute of Informatics, Federal University of Rio Grande do Sul, Brazil (Santos da Silva, Anderson; Wickboldt, Juliano Araujo; Schaeffer-Filho, Alberto)
Abstract	Resilience is the ability of the network to maintain an acceptable level of operation in the face of anomalies, such as malicious attacks, operational overload or misconfigurations. Techniques for anomaly traffic classification are often used to characterize suspicious network traffic, thus supporting anomaly detection schemes in network resilience strategies. In this paper, we extend the PReSET toolset to allow the investigation, comparison and analysis of algorithms for anomaly traffic classification based on machine learning. PReSET was designed to allow the simulation-based evaluation of resilience strategies, thus enabling the comparison of optimal configurations and policies for combating different types of attacks (e.g., DDoS attacks, worms) and other anomalies. In such resilience strategies, policies written in the Ponder2 language can be used to activate/reconfigure traffic classification modules and other mechanisms (e.g., traffic shaping), depending on monitored results in the simulation environment. Our results show that PReSET can be a valuable tool for network operators to evaluate anomaly traffic classification techniques in terms of standard performance metrics.
Starting Page	514
Ending Page	519
File Size	434618
Page Count	6
File Format	PDF
e-ISBN	9781467371940
DOI	10.1109/ISCC.2015.7405566
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Resilience Machine learning algorithms Protocols Feature extraction Computers Ports (Computers) Electronic mail
Content Type	Text
Resource Type	Article

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