DDoS Attack Detection in IoT Networks Using Deep Learning Models Combined with Random Forest as Feature Selector

Abstract

AbstractDue to the major advancements and finesse earned in technology, the internet and communication fields have seen a ground-breaking breakthrough by incorporating themselves with “things” which as a result have created connected systems and have given multiple utilities to a single device. But due to the digitization of technologies and always online and connected features, they are becoming more prone to cyberattacks, mostly due to the widespread Distributed Denial of Services (DDoS) attacks. Since DDoS attacks create multiple agents to attack a victim network, the always-connected Internet of Things (IoT) devices that communicate over various communication protocols gives the perfect platform for such attacks and these sort of the attacks are on the rise day by day. So, the advent of an efficient and highly precise DDoS attack detection model needs to be addressed. Being motivated by the increasing amount of DDoS attacks, the vulnerability of IoT devices to such attacks, and to tackle this situation, two deep learning-based models have been proposed. The models are based on the combination of Random Forest as a feature selector and 1D Convolutional Neural Network and Multilayer Perceptron methods for DDoS attack detection. The main objective behind the proposed models is to detect DDoS attacks accurately and as early as possible. The models have been evaluated using the CICIDS2017 dataset which resulted in high accuracy of 99.63% in the case of RF-1DCNN and 99.58% in the RF-MLP model.KeywordsInternet of Things (IoT)Distributed Denial of Service (DDoS)Intrusion Detection System (IDS)Machine Learning (ML)Deep Learning (DL)Convolutional Neural Network (CNN)Multi-Layer Perceptron (MLP)Random Forest (RF)