KISS: Stochastic Packet Inspection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5537)


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.


Support Vector Machine Packet Payload Deep Packet Inspection Kiss Model Eventual Packet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Politecnico di TorinoItaly
  2. 2.TELECOM ParisTechFrance

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