An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Revealing skype traffic: when randomness plays with you
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Measuring IP and TCP behavior on edge nodes with Tstat
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Communications Magazine
Optimizing Deep Packet Inspection for High-Speed Traffic Analysis
Journal of Network and Systems Management
KISS: stochastic packet inspection classifier for UDP traffic
IEEE/ACM Transactions on Networking (TON)
Kiss to abacus: a comparison of P2P-TV traffic classifiers
TMA'10 Proceedings of the Second international conference on Traffic Monitoring and Analysis
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
Reviewing traffic classification
DataTraffic Monitoring and Analysis
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This paper proposes KISS, a new Internet classification method. Motivated by the expected raise of UDP traffic volume, which stems from the momentum of P2P streaming applications, we propose a novel statistical payload-based classification framework, targeted to UDP traffic. Statistical signatures are automatically inferred from training data, by the means of a Chi-Square like test, which extracts the protocol "syntax", but ignores the protocol semantic and synchronization rules. The signatures feed a decision engine based on Support Vector Machines. KISS is tested in different scenarios, considering both data, VoIP, and traditional P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal 98.7%. Less than 0.05% of False Positives are detected.