Detecting Anomalous Network Traffic with Self-organizing Maps

  • Manikantan Ramadas
  • Shawn Ostermann
  • Brett Tjaden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2820)


Integrated Network-Based Ohio University Network Detective Service (INBOUNDS) is a network based intrusion detection system being developed at Ohio University. The Anomalous Network-Traffic Detection with Self Organizing Maps (ANDSOM) module for INBOUNDS detects anomalous network traffic based on the Self-Organizing Map algorithm. Each network connection is characterized by six parameters and specified as a six-dimensional vector. The ANDSOM module creates a Self-Organizing Map (SOM) having a two-dimensional lattice of neurons for each network service. During the training phase, normal network traffic is fed to the ANDSOM module, and the neurons in the SOM are trained to capture its characteristic patterns. During real-time operation, a network connection is fed to its respective SOM, and a “winner” is selected by finding the neuron that is closest in distance to it. The network connection is then classified as an intrusion if this distance is more than a pre-set threshold.


Intrusion Detection Anomaly Detection Self-Organizing Maps 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Manikantan Ramadas
    • 1
  • Shawn Ostermann
    • 1
  • Brett Tjaden
    • 2
  1. 1.Ohio University 
  2. 2.James Madison University 

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