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An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks

Author

Listed:
  • Xuejian Zhao

    (Technology and Application Engineering Center of Postal Big Data, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

  • Huiying Su

    (Technology and Application Engineering Center of Postal Big Data, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

  • Zhixin Sun

    (Technology and Application Engineering Center of Postal Big Data, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Abstract

A SDN (Software-Defined Network) separates the control layer from the data layer to realize centralized network control and improve the scalability and the programmability. SDN also faces a series of security threats. An intrusion detection system (IDS) is an effective means of protecting communication networks against traffic attacks. In this paper, a novel IDS model for SDN is proposed to collect and analyze the traffic which is generally at the control plane. Moreover, network congestion will occur when the amount of data transferred reaches the data processing capacity of the IDS. The suggested IDS model addresses this problem with a probability-based traffic sampling method in which the genetic algorithm (GA) is used to approach the sampling probability of each sampling point. According to the simulation results, the suggested IDS model based on GA is capable of enhancing the detection efficiency in SDNs.

Suggested Citation

  • Xuejian Zhao & Huiying Su & Zhixin Sun, 2022. "An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3941-:d:951758
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    Citations

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    Cited by:

    1. Dusmurod Kilichev & Wooseong Kim, 2023. "Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO," Mathematics, MDPI, vol. 11(17), pages 1-31, August.
    2. Walid I. Khedr & Ameer E. Gouda & Ehab R. Mohamed, 2023. "P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture," Mathematics, MDPI, vol. 11(16), pages 1-36, August.

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