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A distributed SDN-based intrusion detection system for IoT using optimized forests

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  • Ke Luo

Abstract

Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods.

Suggested Citation

  • Ke Luo, 2023. "A distributed SDN-based intrusion detection system for IoT using optimized forests," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-21, August.
  • Handle: RePEc:plo:pone00:0290694
    DOI: 10.1371/journal.pone.0290694
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