Anomaly Detection of Distributed Denial of Service (DDoS) in IoT Network Using Machine Learning

Abstract

Abstract This research focuses on developing an anomaly detection system using machine learning to mitigate Distributed Denial of Service (DDoS) attacks in IoT networks. The study utilizes a diverse dataset from IoT environments to train and evaluate machine learning algorithms for DDoS detection. The dataset includes various IoT device types, communication protocols, and network configurations. The research aims to achieve several objectives, including dataset preprocessing, feature engineering, machine learning model selection, anomaly detection, and performance evaluation. The research team preprocesses the raw Internet of Things (IoT) network data by cleaning and transforming it to prepare it for analysis. They then extract relevant features from the data to effectively characterize normal and abnormal network behavior. Multiple machine learning algorithms are evaluated and compared to determine the most suitable models for DDoS detection in IoT networks. The selected machine learning models are then used to identify and classify abnormal traffic patterns associated with DDoS attacks. The performance of the developed anomaly detection system is evaluated by assessing its accuracy, precision, recall, and F1 score. The significance of this research lies in its potential to enhance the security of IoT networks by proactively detecting and mitigating DDoS attacks. By leveraging machine learning, the study aims to provide a robust defense mechanism against this pervasive threat, ensuring the reliability and availability of IoT services and applications.