Detection of DDoS Attacks on Urban IoT Devices Using Neural Networks

Simon Onyebuchi Obetta, Arghir-Nicolae Moldovan

2023

Abstract

As the Internet of Things (IoT) has grown in recent years, attackers are increasingly targeting IoT devices to perform malicious attacks such as DDoS. Often, this is due to inadequate security implementation and management of IoT devices. Sometimes, the infected IoT devices can be used as bots by attackers to launch a DDoS attack on a target. Although various security methods have been introduced for IoT devices, effective DDoS detection methods are still required. This paper compares the performance of four machine learning algorithms for DDoS detection on a recent Urban IoT dataset: Feedforward Neural Network (FNN), Deep Neural Network (DNN), Autoencoder (AEN) and Random Forest (RF). The results show that DNN achieved the highest accuracy of 95.9% on train data and 88.6% on test data.

Paper Citation

in Harvard Style

Obetta S. and Moldovan A. (2023). Detection of DDoS Attacks on Urban IoT Devices Using Neural Networks. In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-643-9, SciTePress, pages 236-242. DOI: 10.5220/0011998900003482

in Bibtex Style

@conference{iotbds23,
author={Simon Onyebuchi Obetta and Arghir-Nicolae Moldovan},
title={Detection of DDoS Attacks on Urban IoT Devices Using Neural Networks},
booktitle={Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2023},
pages={236-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011998900003482},
isbn={978-989-758-643-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Detection of DDoS Attacks on Urban IoT Devices Using Neural Networks
SN - 978-989-758-643-9
AU - Obetta S.
AU - Moldovan A.
PY - 2023
SP - 236
EP - 242
DO - 10.5220/0011998900003482
PB - SciTePress