DDoS Attack Detection in Edge-IIoT Network using Ensemble Learning

Abstract

As the number of IoT devices increases daily due to the rapid growth in technology, every device and network is vulnerable to attacks because it is exposed to the internet. Denial of Service (DoS) is a prevalent type of intrusion on the Internet of Things (IoT) network in which the server becomes down due to flooding requests. Distributed Denial of Service (DDoS) is a special type of DoS attack where the network of malicious computers called botnet consumes the target's system resources by flooding the requests. Edge computing is closely related to Industrial Internet of Things (IIoT), and industry 4.0. Both of them are relatively emerging technologies so security is a crucial part of them. By incorporating our contributions to the current and innovative dataset Edge-IIoT, the proposed study presents a novel approach to detect DDoS attacks in an IIoT network in the domain of edge computing, whether the traffic is normal or malicious (DDoS traffic). This study explores various Ensemble Learning (EL) techniques to predict normal and malicious DDoS traffic along with the type of DDoS attack. The study applies various preprocessing techniques like Synthetic Minority Over Sampling Technique (SMOTE), label encoding, etc. to enhance the model's performance and reveals how EL techniques performs better in terms of accuracy than the individual classifiers. Further, the performance of all EL techniques has been investigated in terms of all evaluation measures, including the elapsed time. This important addition not only broadens the focus of study in this area but also offers insightful comparisons of the efficiency and precision of various ensemble approaches as well as individual classifiers. The study achieved a maximum of 99.99% in all evaluation measures.