Enhanced Machine Learning-Based SDN Controller Framework for Securing IoT Networks

Karmous, Neder; Aoueileyine, Mohamed Ould-Elhassen; Abdelkader, Manel; Youssef, Neji

doi:10.1007/978-3-031-28694-0_6

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Their proposed algorithms achieve accuracy of 90%. Karmous et al [16] presented TC methods using unique ML models on SDN architecture by applying five distinct ML models (kNN, SVM, DT, and NB). The highest accuracy between them was 99%.…”

Section: Related Workmentioning

confidence: 99%

Traffic Classification in Software Defined Networks based on Machine Learning Algorithms

mahgoub,

Ashour,

Yakout

et al. 2024

International Journal of Telecommunications

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A crucial area of research is traffic classification, particularly in light of the advancements in machine learning in software-defined networking. Software-defined networks, which divide the control and data planes, can be automated and controlled by machine learning. Because traditional procedures could not keep up with the expanding use of encryption, the use of techniques for this purpose has increased. In this study, 15 features (the quantity of packets communicated, the average transmission time, and the number of instantly transmitted packets) were used to build traffic flows on the SDN for several protocols, including WWW, DNS, FTP, ICMP, P2P, and VOIP. A real-time dataset was produced by gathering data based on the features that were generated over the SDN controller in the physical network. We use the dataset to test and train a variety of machine learning models, including Random Forest, K Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, and Naive Bayes. With a 99.8% accuracy rate, Decision Tree emerged as the most successful model for the traffic classification challenge. In order to provide the best classification performance with the lowest cost flow features for traffic classification in SDN, this approach has been identified as machine learning.

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Section: Related Workmentioning

confidence: 99%

Traffic Classification in Software Defined Networks based on Machine Learning Algorithms

mahgoub,

Ashour,

Yakout

et al. 2024

International Journal of Telecommunications

View full text Add to dashboard Cite

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Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices

Karmous,

Aoueileyine,

Abdelkader

et al. 2024

Sensors

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The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people’s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices’ security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

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Enhanced Machine Learning-Based SDN Controller Framework for Securing IoT Networks

Cited by 2 publications

References 32 publications

Traffic Classification in Software Defined Networks based on Machine Learning Algorithms

Traffic Classification in Software Defined Networks based on Machine Learning Algorithms

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices

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