{"title":"物联网环境中基于机器学习的 SDN DDOS 攻击检测与缓解","authors":"","doi":"10.1016/j.jfranklin.2024.107197","DOIUrl":null,"url":null,"abstract":"<div><p>Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based DDOS attack detection and mitigation in SDNs for IoT environments\",\"authors\":\"\",\"doi\":\"10.1016/j.jfranklin.2024.107197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.</p></div>\",\"PeriodicalId\":17283,\"journal\":{\"name\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0016003224006185\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224006185","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
软件定义网络(SDN)平台在提供安全防范当今互联网攻击方面发挥着关键作用。SDN 将控制平面与数据平面解耦,以最大限度地提高网络性能。DDOS 攻击是基于云的网络中的多种攻击之一。在大规模云网络中,SDN 在控制 DDoS 攻击和保护物联网节点等终端节点以及其他计算设备方面发挥着至关重要的作用。本文提供了一种利用机器学习算法检测和预防 DDoS 攻击的有效方法。本文分析了 SDN 在物联网系统中的性能,其中包含大量使用多控制器的计算设备。它还提出了一种处理 DDoS 攻击的有效方法。DDoS 攻击来自基础设施层的物联网终端设备,通过 SDN 控制的测试平台攻击资源。在准确有效地检测和缓解泛洪 DDoS 攻击方面,所提出的 ML 方法优于现有方法,准确率高达 99.99%。此外,还将提出的工作结果与其他文章的结果进行了比较,以证明结果的有效性。
Machine learning-based DDOS attack detection and mitigation in SDNs for IoT environments
Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.