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

Frias-Martinez, Vanessa; Stolfo, Salvatore; Keromytis, Angelos D.

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.


Also Published In

Information security: 11th international conference, ISC 2008, Taipei, Taiwan, September 15-18, 2008

More About This Work

Academic Units
Computer Science
Columbia University Computer Science Technical Reports, CUCS-018-08
Published Here
April 20, 2010