Ensemble Learning-Based DDOS Attack Recognition in IoT Networks

Abstract

According to a quote by Brendan O' Brien," If you think the Internet has changed your life, think again. The Internet of Things is about to change it all over again!". By improving data analytics, IoT operations, and human–machine interaction, the Internet of Things (IoT) and Artificial Intelligence (AI) are coming together to form AIoT, which is transforming modern production in the era of Industry 4.0. Although AIoT promises more sustainability, efficiency, and safety, the increasing use of IoT devices also increases their susceptibility to cyberthreats, among which Distributed Denial-of-Service (DDoS) attacks are among the most common. Unlocking the full potential of AIoT in linked and industrial environments requires addressing these security issues. In this paper, we leverage the publicly available CIC IoT 2023 dataset to conduct a comprehensive analysis of IoT-based cyber threats, focusing on the detection of seven major attack types and their respective subcategories. To guarantee the accuracy and applicability of the input data, a thorough feature extraction procedure was carried out. To evaluate detection performance, we applied a diverse set of six machine learning and deep learning models. Notably, the most successful approach was an ensemble learning strategy, which produced better accuracy and resilience. Thorough validation procedures were used to verify the results' generalizability and dependability, highlighting the promise of advanced learning methods in fortifying AIoT security infrastructures. Our research indicates that ensemble learning and deep learning models are a promising option for implementation in practical AIoT security frameworks as, when appropriately set up, they provide notable benefits for processing and categorizing tabular IoT data.