The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the characteristics and origins of internet flow rates
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
ACM SIGCOMM Computer Communication Review
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
A model of cognitive loads in massively multiplayer online role playing games
Interacting with Computers
Bro: a system for detecting network intruders in real-time
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
A markovian signature-based approach to IP traffic classification
Proceedings of the 3rd annual ACM workshop on Mining network data
Revealing skype traffic: when randomness plays with you
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
SS'08 Proceedings of the 17th conference on Security symposium
3G Evolution, Second Edition: HSPA and LTE for Mobile Broadband
3G Evolution, Second Edition: HSPA and LTE for Mobile Broadband
A nonlinear, recurrence-based approach to traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Characterizing network traffic by means of the NetMine framework
Computer Networks: The International Journal of Computer and Telecommunications Networking
Efficient application identification and the temporal and spatial stability of classification schema
Computer Networks: The International Journal of Computer and Telecommunications Networking
Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Profiling and identification of P2P traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
McPAD: A multiple classifier system for accurate payload-based anomaly detection
Computer Networks: The International Journal of Computer and Telecommunications Networking
Support Vector Machines for TCP traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Automatic dimensionality selection from the scree plot via the use of profile likelihood
Computational Statistics & Data Analysis
A tactile emotional interface for instant messenger chat
Proceedings of the 2007 conference on Human interface: Part II
Detection of auto programs for MMORPGs
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Traffic classification using a statistical approach
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
A parameterizable methodology for Internet traffic flow profiling
IEEE Journal on Selected Areas in Communications
Deep packet inspection tools and techniques in commodity platforms: Challenges and trends
Journal of Network and Computer Applications
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Accurately classifying and identifying wireless network traffic associated with various applications, such as Web, VoIP, and VoD, is a challenge for both service providers and network operators. Traditional classification schemes exploiting port or payload analysis are becoming ineffective in actual networks, as many new applications are emerging. This paper presents the classification of HSDPA network traffic applications using Classification and Regression Tree (CART) and Support Vector Machine (SVM) with the session information as a basic measure. The session is bidirectional traffic stream between two hosts that is used as a basic measure and a unit of information. We acquired and processed HSDPA traffic from a real 3G network without sanitizing the data. CART and SVM are used to classify six application groups (download, game, upload, VoD, VoiP, and web) with a set of twelve easily retrievable features. These features are composed of simple statistical pieces of information, such as the standard deviation of the packet sizes, the number of packets, and the duration of a session. Compared to results of a flow-based application classification, session-based classification produces 11.07% (CART) and 21.99% (SVM) increases in the true positive rate. This feature set is further reduced to two principal components using Principal Component Regression. This paper also takes the initiative to compare CART to K-Means, the wired network traffic clustering scheme, and shows that CART is more accurate for classification than is K-Means.