Bayesian neural networks for internet traffic classification.
Internet traffic identification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models. We present here a traffic classifier that can achieve a high accuracy across a range of application types without any source or destination host-address or port information. We use supervised machine learning based on a Bayesian trained neural network. Though our technique uses training data with categories derived from packet content, training and testing were done using features derived from packet streams consisting of one or more packet headers. By providing classification without access to the contents of packets, our technique offers wider application than methods that require full packet/payloads for classification. This is a powerful advantage, using samples of classified traffic to permit the categorization of traffic based only upon commonly available information.
Auld
Tom
T
Department of Physics, University of Cambridge, Cambridge CB3 OHE, UK. tauld@mrao.cam.ac.uk
Moore
Andrew W
AW
Gull
Stephen F
SF
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D016494:Computer Security; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted