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

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.

Cyberattacks Detection in IoMT using Machine Learning Techniques

“…The third layer performs the measurement of live network traffic for the detection of DDoS attacks. In experimental analysis, the authors showed that the proposed model achieved higher accuracy than other state-of-the-art approaches [15].…”

An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things

“…MUD also used the blockchain security model to transfer data with the aid of IoT devices via the Hyperledger platform. The security architecture for the dynamic and on-demand administration for virtualized identification, authority, and accountability in SDN-enabled IoT networks was additionally provided by [ 20 , 21 ]. The authors accomplished efficient IoT device bootstrap and fine-grained administration of their network access control.…”

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

“…Similarly, work in [ 12 ] discusses various machine learning techniques for DDoS and intrusion prevention in SDN and provides a comparison between these techniques. Similar attacks are detected with the help of machine learning techniques in [ 29 ] where authors use SVM, NB, KNN, RF, and LR.…”