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SNIPS: A Software-Defined Approach for Scaling Intrusion Prevention Systems via Offloading

  • Victor Heorhiadi
  • Seyed Kaveh Fayaz
  • Michael K. Reiter
  • Vyas Sekar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8880)

Abstract

Growing traffic volumes and the increasing complexity of attacks pose a constant scaling challenge for network intrusion prevention systems (NIPS). In this respect, offloading NIPS processing to compute clusters offers an immediately deployable alternative to expensive hardware upgrades. In practice, however, NIPS offloading is challenging on three fronts in contrast to passive network security functions: (1) NIPS offloading can impact other traffic engineering objectives; (2) NIPS offloading impacts user perceived latency; and (3) NIPS actively change traffic volumes by dropping unwanted traffic. To address these challenges, we present the SNIPS system. We design a formal optimization framework that captures tradeoffs across scalability, network load, and latency. We provide a practical implementation using recent advances in software-defined networking without requiring modifications to NIPS hardware. Our evaluations on realistic topologies show that SNIPS can reduce the maximum load by up to 10× while only increasing the latency by 2%.

Keywords

Intrusion Detection Intrusion Detection System Drop Rate Network Intrusion Detection Linear Programming Solution 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Victor Heorhiadi
    • 1
  • Seyed Kaveh Fayaz
    • 2
  • Michael K. Reiter
    • 1
  • Vyas Sekar
    • 2
  1. 1.UNC Chapel HillChapel HillUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

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