Abstract
The rapid growth ofThe Internet of Things (IoT), intelligent devices, and 5G networks has increased the prevalence and complexity of Distributed Denial of Service (DDoS) attacks, posing significantchallenges to cybersecurity. The objective of this research is to develop an effective method to detect and prevent DDoS attacks, thereby safeguarding communication systems from such threats. The proposed "Intelligent Distributed Denial of Service AttacksDetection (IDDOSAD) Approach"utilizes supervised machine learning algorithms, includingRandom Forests, Decision Trees, K-Nearest Neighbor, XGBoost, and Support Vector Machine, along with ensemble learning to enhance detection accuracy. Themodel development process consists of datacollection, pre-processing, splitting into training and testing sets, selecting prediction models, and evaluating their performance. Evaluated on a dataset of 11,423 instances, the proposedapproach demonstrated promising results, with accuracy ranging from 92% to 100% for the time series dataset. In conclusion, the proposedapproach effectively detects and mitigates DDoS attacks, offering a reliable solution to protect communication systems against this growing cybersecurity threat
Recommended Citation
Alsumaidaie, Mustafa S. Ibrahim; Alheeti, Khattab M. Ali; and Alaloosy, Abdul Kareem
(2023)
"Intelligent Detection of Distributed Denial of Service Attacks: A Supervised Machine Learning and Ensemble Approach,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
3, Article 2.
DOI: https://doi.org/10.52866/ijcsm.2023.02.03.002
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss3/2
