CANVuS: Context-Aware Network Vulnerability Scanning

  • Yunjing Xu
  • Michael Bailey
  • Eric Vander Weele
  • Farnam Jahanian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6307)


Enterprise networks face a variety of threats including worms, viruses, and DDoS attacks. Development of effective defenses against these threats requires accurate inventories of network devices and the services they are running. Traditional vulnerability scanning systems meet these requirements by periodically probing target networks to discover hosts and the services they are running. This polling-based model of vulnerability scanning suffers from two problems that limit its effectiveness—wasted network resources and detection latency that leads to stale data. We argue that these limitations stem primarily from the use of time as the scanning decision variable. To mitigate these problems, we instead advocate for an event-driven approach that decides when to scan based on changes in the network context—an instantaneous view of the host and network state. In this paper, we propose an architecture for building network context for enterprise security applications by using existing passive data sources and common network formats. Using this architecture, we built CANVuS, a context-aware network vulnerability scanning system that triggers scanning operations based on changes indicated by network activities. Experimental results show that this approach outperforms the existing models in timeliness and consumes much fewer network resources.


Target Network Detection Latency Enterprise Network Network Context Attack Graph 
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 2010

Authors and Affiliations

  • Yunjing Xu
    • 1
  • Michael Bailey
    • 1
  • Eric Vander Weele
    • 1
  • Farnam Jahanian
    • 1
  1. 1.Computer Science and EngineeringUniversity of MichiganAnn ArborUSA

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