IoT-DH dataset for classification, identification, and detection DDoS attack in IoT

Abstract

The proliferation of Internet of Things devices has ushered in a new era of connectivity and con-venience, yet it has also exposed a myriad of security challenges, with Distributed Denial of Service attacks posing a significant threat. This paper introduces the IoT-DH dataset, a novel and extensive dataset designed for the purpose of classifying, identifying, and detecting DDoS attacks within IoT ecosystems. The dataset encompasses diverse scenarios and network configurations, providing a realistic representation of IoT environments. We present a systematic analysis of the IoT-DH dataset, exploring its features and characteristics that mirror the complexities of real-world IoT net-works. The dataset includes a variety of attack scenarios, incorporating different attack vectors and intensities to capture the evolving nature of DDoS threats in IoT. Our approach facilitates the development and evaluation of robust machine learning and deep learning models for effective DDoS attack mitigation. Furthermore, we propose a multi-faceted methodology for leveraging the IoT-DH dataset, encompassing classification techniques to categorize attack types, identification mechanisms to pinpoint malicious entities, and detection algorithms to promptly respond to ongoing DDoS incidents. The efficacy of these methodologies is demonstrated through extensive experiments and evaluations, showcasing their ability to enhance the security posture of IoT environments.