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SDN-Based Kernel Modular Countermeasure for Intrusion Detection

  • Tommy Chin
  • Kaiqi XiongEmail author
  • Mohamed Rahouti
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 238)

Abstract

Software-Defined Networking (SDN) is a core technology. However, Denial of Service (DoS) has been proved a serious attack in SDN environments. A variety of Intrusion Detection and Prevention Systems (IDPS) have been proposed for the detection and mitigation of DoS threats, but they often present significant performance overhead and long mitigation time so as to be impractical. To address these issues, we propose KernelDetect, a lightweight kernel-level intrusion detection and prevention framework. KernelDetect leverages modular string searching and filtering mechanisms with SDN techniques. By considering that the Aho-Corasick and Bloom filter are exact string matching and partial matching techniques respectively, we design KernelDetect to leverage the strengths of both algorithms with SDN. Moreover, we compare KernelDetect with traditional IDPS: SNORT and BRO, using a real-world testbed. Comprehensive experimental studies demonstrate that KernelDetect is an efficient mechanism and performs better than SNORT and BRO in threat detection and mitigation.

Keywords

Aho-Corasick Bloom filters Intrusion detection system Security Software Defined Networking (SDN) 

Notes

Acknowledgments

We acknowledge National Science Foundation (NSF) to partially sponsor the work under grants #1633978, #1620871, #1620862, and #1636622, and BBN/GPO project #1936 through NSF/CNS grant. We also thank the Florida Center for Cybersecurity for a seed grant. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of NSF.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.University of South FloridaTampaUSA

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