Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT

Muhammad Aslam; Dengpan Ye; Aqil Tariq; Muhammad Asad; Muhammad Hanif; David Ndzi; Samia Allaoua Chelloug; Mohamed Abd Elaziz; Mohammed A A Al-Qaness; Syeda Fizzah Jilani

doi:10.3390/s22072697

Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT

Sensors (Basel). 2022 Mar 31;22(7):2697. doi: 10.3390/s22072697.

Authors

Affiliations

¹ School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Glasgow G72 0LH, UK.
² School of Cyber Sceince and Engineering, Wuhan University, Wuhan 430079, China.
³ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.
⁴ Department of Computer Science, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
⁵ Department of Computer Science, COMSATS University of Islamabad, Wah Cantt 45550, Pakistan.
⁶ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁷ Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.
⁸ Department of Physics, Aberystwyth University, Aberystwyth SY23 3FL, UK.

Abstract

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

Keywords: Distributed Denial-of-Services; Internet of Things; adaptive machine learning; detection; mitigation; network security; software defined networking.