An optimization-inspired intrusion detection model for software-defined networking
As an emerging network architecture, software-defined networking (SDN) has the core concept of separating the control plane from the network hardware and unifying its management by a central controller. Since the centralized control of SDN is such that an attack on the controller can lead to the par...
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| Language: | English |
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AIMS Press
2025-01-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2025012 |
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| author | Hui Xu Longtan Bai Wei Huang |
| author_facet | Hui Xu Longtan Bai Wei Huang |
| author_sort | Hui Xu |
| collection | DOAJ |
| description | As an emerging network architecture, software-defined networking (SDN) has the core concept of separating the control plane from the network hardware and unifying its management by a central controller. Since the centralized control of SDN is such that an attack on the controller can lead to the paralysis of the entire network, intrusion detection has become particularly significant for SDN. Currently, more and more intrusion detection systems based on machine learning and deep learning are being applied to SDN, but most have drawbacks such as complex models and low detection accuracy. This paper proposes an enhanced spider wasp optimizer (ESWO) algorithm for feature dimensionality reduction of intrusion detection datasets and constructs a new intrusion detection model (IDM), namely ESWO-IDM, for SDN. The ESWO algorithm integrates multiple strategies, including tent chaotic map strategy and elite opposition learning strategy to improve the diversity of the population, Lévy flight strategy to prevent the algorithm from falling into local optimum in the early stage, and dynamic adjustment strategy of control parameters to balance exploration and exploitation of the algorithm. ESWO was empirically evaluated using eight benchmark test functions and four UCI datasets to comprehensively demonstrate its advantages. Binary and multiclassification experiments were conducted using the InSDN dataset to analyze the ESWO-IDM performance and compare it with other IDMs. The experimental results show that the ESWO-IDM achieves the best performance in all the metrics in both binary classification and multiclassification and has the most prominent effect on the detection of normal, denial of service (DoS), distributed DoS, and Brute Force Attack types, which effectively improves SDN intrusion detection from the viewpoint of optimization. |
| format | Article |
| id | doaj-art-44847cf61ffb41c59f8cc3fcbf3dbd1e |
| institution | DOAJ |
| issn | 2688-1594 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-44847cf61ffb41c59f8cc3fcbf3dbd1e2025-08-20T03:17:09ZengAIMS PressElectronic Research Archive2688-15942025-01-0133123125410.3934/era.2025012An optimization-inspired intrusion detection model for software-defined networkingHui Xu0Longtan Bai1Wei Huang2School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaAs an emerging network architecture, software-defined networking (SDN) has the core concept of separating the control plane from the network hardware and unifying its management by a central controller. Since the centralized control of SDN is such that an attack on the controller can lead to the paralysis of the entire network, intrusion detection has become particularly significant for SDN. Currently, more and more intrusion detection systems based on machine learning and deep learning are being applied to SDN, but most have drawbacks such as complex models and low detection accuracy. This paper proposes an enhanced spider wasp optimizer (ESWO) algorithm for feature dimensionality reduction of intrusion detection datasets and constructs a new intrusion detection model (IDM), namely ESWO-IDM, for SDN. The ESWO algorithm integrates multiple strategies, including tent chaotic map strategy and elite opposition learning strategy to improve the diversity of the population, Lévy flight strategy to prevent the algorithm from falling into local optimum in the early stage, and dynamic adjustment strategy of control parameters to balance exploration and exploitation of the algorithm. ESWO was empirically evaluated using eight benchmark test functions and four UCI datasets to comprehensively demonstrate its advantages. Binary and multiclassification experiments were conducted using the InSDN dataset to analyze the ESWO-IDM performance and compare it with other IDMs. The experimental results show that the ESWO-IDM achieves the best performance in all the metrics in both binary classification and multiclassification and has the most prominent effect on the detection of normal, denial of service (DoS), distributed DoS, and Brute Force Attack types, which effectively improves SDN intrusion detection from the viewpoint of optimization.https://www.aimspress.com/article/doi/10.3934/era.2025012software-defined networkingintrusion detectionoptimizationspider wasp optimizer |
| spellingShingle | Hui Xu Longtan Bai Wei Huang An optimization-inspired intrusion detection model for software-defined networking Electronic Research Archive software-defined networking intrusion detection optimization spider wasp optimizer |
| title | An optimization-inspired intrusion detection model for software-defined networking |
| title_full | An optimization-inspired intrusion detection model for software-defined networking |
| title_fullStr | An optimization-inspired intrusion detection model for software-defined networking |
| title_full_unstemmed | An optimization-inspired intrusion detection model for software-defined networking |
| title_short | An optimization-inspired intrusion detection model for software-defined networking |
| title_sort | optimization inspired intrusion detection model for software defined networking |
| topic | software-defined networking intrusion detection optimization spider wasp optimizer |
| url | https://www.aimspress.com/article/doi/10.3934/era.2025012 |
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