Behavior-Based Network Access Control: A Proof-of-Concept

  • Vanessa Frias-Martinez
  • Salvatore J. Stolfo
  • Angelos D. Keromytis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5222)


Current NAC technologies implement a pre-connect phase where the status of a device is checked against a set of policies before being granted access to a network, and a post-connect phase that examines whether the device complies with the policies that correspond to its role in the network. In order to enhance current NAC technologies, we propose a new architecture based on behaviors rather than roles or identity, where the policies are automatically learned and updated over time by the members of the network in order to adapt to behavioral changes of the devices. Behavior profiles may be presented as identity cards that can change over time. By incorporating an Anomaly Detector (AD) to the NAC server or to each of the hosts, their behavior profile is modeled and used to determine the type of behaviors that should be accepted within the network. These models constitute behavior-based policies. In our enhanced NAC architecture, global decisions are made using a group voting process. Each host’s behavior profile is used to compute a partial decision for or against the acceptance of a new profile or traffic. The aggregation of these partial votes amounts to the model-group decision. This voting process makes the architecture more resilient to attacks. Even after accepting a certain percentage of malicious devices, the enhanced NAC is able to compute an adequate decision. We provide proof-of-concept experiments of our architecture using web traffic from our department network. Our results show that the model-group decision approach based on behavior profiles has a 99% detection rate of anomalous traffic with a false positive rate of only 0.005%. Furthermore, the architecture achieves short latencies for both the pre- and post-connect phases.


Network Access Control Technologies Intrusion Detection Systems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vanessa Frias-Martinez
    • 1
  • Salvatore J. Stolfo
    • 1
  • Angelos D. Keromytis
    • 1
  1. 1.Computer Science DepartmentColumbia University 

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