Machine Learning-Based Network Detection Research for SDNs
This research endeavors to fortify the security posture of Software-Defined Networks (SDN) through the strategic utilization of intelligent machine learning techniques, with a primary focus on mitigating detrimental Denial of Service (DoS) attacks. To accomplish this, this study constructed a rigoro...
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01015.pdf |
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| Summary: | This research endeavors to fortify the security posture of Software-Defined Networks (SDN) through the strategic utilization of intelligent machine learning techniques, with a primary focus on mitigating detrimental Denial of Service (DoS) attacks. To accomplish this, this study constructed a rigorously designed simulated SDN environment, which served as the cornerstone for meticulously assembling a comprehensive dataset encompassing a diverse array of attack vectors, with particular emphasis on DoS. Employing a tactical blend of established and cutting-edge machine learning algorithms, including Random Forest, Logistic Regression, and Decision Tree, alongside the advanced XGBoost and LightGBM models, this study conducted an exhaustive investigation to pinpoint the most efficacious methods for swiftly and precisely identifying DoS threats. It is necessary to note that XGBoost and LightGBM demonstrate an astonishing level of multiple performance, which testifies their outstanding ability to enhance SDN security. Reasserting the idea of the critically important role of machine learning for securing SDNs against possible intrusions, these results point not only to the highly beneficial applications of machine learning for protecting SDNs against malicious intrusions but also its indispensable role in preserving network stability and optimizing performance. Moreover, it emphasizes the operational advantage of deploying multiple organic sets of machine learning algorithms, which can achieve even greater precision and efficiency than individual machine learning algorithms in practical uses, bringing it closer to developing a more robust and secure SDN environment. |
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| ISSN: | 2271-2097 |