A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things

Bassam Kasasbeh, Hadeel Ahmad

2023

Abstract

Internet of Things (IoT) devices are vulnerable to a wide range of unique security risks during the data collection and transmission processes. Due to a lack of resources, these devices increased the attack surface and made it easier for an attacker to find a target. The Denial of Service (DoS) attack is one of the most common attacks that can target all layers of the IoT protocol. Therefore, Intrusion Detection Systems (IDS) based on machine learning (ML) are the best ways to confront these risks. However, an imbalanced dataset for cyber attacks makes it difficult to detect them with ML models. We propose an undersampling technique that clusters the data set using Fuzzy C-means (FCM) and picks similar instances with the same features to ensure the integrity of the dataset. We used accuracy, precision, sensitivity, specificity, F-measure, AUC, and G-means to determine how good the results were. The proposed technique had 97.6% overall accuracy. Furthermore, it got 96.94%, 96.39%, 99.59%, 98.08%, and 97.16% True Positive Rates (TPR) in the Blackhole, Grayhole, Flooding, Scheduling, and Normal (no attacks) classes, respectively. The results show that the proposed method for detecting DoS attacks in the IoT has succeeded.

Paper Citation

in Harvard Style

Kasasbeh B. and Ahmad H. (2023). A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 861-868. DOI: 10.5220/0011786300003393

in Bibtex Style

@conference{icaart23,
author={Bassam Kasasbeh and Hadeel Ahmad},
title={A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={861-868},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011786300003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things
SN - 978-989-758-623-1
AU - Kasasbeh B.
AU - Ahmad H.
PY - 2023
SP - 861
EP - 868
DO - 10.5220/0011786300003393