On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Data networks as cascades: investigating the multifractal nature of Internet WAN traffic
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Dynamics of IP traffic: a study of the role of variability and the impact of control
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
A Longitudinal Study of P2P Traffic Classification
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Infinitely divisible cascade analysis of network traffic data
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Detection of illicit traffic based on multiscale analysis
SoftCOM'09 Proceedings of the 17th international conference on Software, Telecommunications and Computer Networks
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In the last few years, several new IP applications and protocols emerged as the capability of the networks to provide new services increased. The rapid increase in the number of users of Peer-to-Peer (P2P) network applications, due to the fact that users are easily able to use network resources over these overlay networks, also lead to a drastic increase in the overall Internet traffic volume. An accurate mapping of Internet traffic to applications can be important for a broad range of network management and measurement tasks, including traffic engineering, service differentiation, performance/failure monitoring and security. Traditional mapping approaches have become increasingly inaccurate because many applications use non-default or ephemeral port numbers, use well-known port numbers associated with other applications, change application signatures or use traffic encryption. This paper presents a novel framework for identifying IP applications based on the multiscale behavior of the generated traffic: by performing clustering analysis over the multiscale parameters that are inferred from the measured traffic, we are able to efficiently differentiate different IP applications. Besides achieving accurate identification results, this approach also avoids some of the limitations of existing identification techniques, namely their inability do deal with stringent confidentiality requirements.