Applying modified golden jackal optimization to intrusion detection for Software-Defined Networking

Feng Qiu; Hui Xu; Fukui Li; Feng Qiu; Hui Xu; Fukui Li

doi:10.3934/era.2024021

Electronic Research Archive

2024, Volume 32, Issue 1: 418-444. doi: 10.3934/era.2024021

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Applying modified golden jackal optimization to intrusion detection for Software-Defined Networking

School of Computer Science, Hubei University of Technology, Wuhan 430068, China

Received: 14 November 2023 Revised: 12 December 2023 Accepted: 19 December 2023 Published: 28 December 2023

As a meta-heuristic algorithm, the Golden Jackal Optimization (GJO) algorithm has been widely used in traditional network intrusion detection due to its ease of use and high efficiency. This paper aims to extend its application to the emerging field of Software-Defined Networking (SDN), which is a new network architecture. To adapt the GJO for SDN intrusion detection, a modified Golden Jackal Optimization (mGJO) is proposed to enhance its performance with the use of two strategies. First, an Elite Dynamic Opposite Learning strategy operates during each iteration to find solutions opposite to the current global optimal solutions, which increases population diversity. Second, an updating strategy based on the Golden Sine II Algorithm is utilized in the exploitation phase to update the position information of the golden jackal pairs, which accelerates the search for the best feature subset indexes. To validate the feasibility of the mGJO algorithm, this paper first assesses its optimization capability using benchmark test functions. Then, four UCI datasets and the NSL-KDD dataset are used to test the classification capability of the mGJO algorithm and its application in traditional network intrusion detection. Furthermore, the InSDN dataset is used to validate the feasibility of the mGJO algorithm for SDN intrusion detection. The experimental results show that, when the mGJO algorithm is applied to SDN for intrusion detection, the various indexes of classification and the selection of feature subsets achieve better results.
- Software-Defined Networking,
- intrusion detection,
- feature selection,
- Golden Jackal Optimization,
- Elite Dynamic Opposite Learning,
- Golden Sine II Algorithm
Citation: Feng Qiu, Hui Xu, Fukui Li. Applying modified golden jackal optimization to intrusion detection for Software-Defined Networking[J]. Electronic Research Archive, 2024, 32(1): 418-444. doi: 10.3934/era.2024021

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Abstract

As a meta-heuristic algorithm, the Golden Jackal Optimization (GJO) algorithm has been widely used in traditional network intrusion detection due to its ease of use and high efficiency. This paper aims to extend its application to the emerging field of Software-Defined Networking (SDN), which is a new network architecture. To adapt the GJO for SDN intrusion detection, a modified Golden Jackal Optimization (mGJO) is proposed to enhance its performance with the use of two strategies. First, an Elite Dynamic Opposite Learning strategy operates during each iteration to find solutions opposite to the current global optimal solutions, which increases population diversity. Second, an updating strategy based on the Golden Sine II Algorithm is utilized in the exploitation phase to update the position information of the golden jackal pairs, which accelerates the search for the best feature subset indexes. To validate the feasibility of the mGJO algorithm, this paper first assesses its optimization capability using benchmark test functions. Then, four UCI datasets and the NSL-KDD dataset are used to test the classification capability of the mGJO algorithm and its application in traditional network intrusion detection. Furthermore, the InSDN dataset is used to validate the feasibility of the mGJO algorithm for SDN intrusion detection. The experimental results show that, when the mGJO algorithm is applied to SDN for intrusion detection, the various indexes of classification and the selection of feature subsets achieve better results.

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