Enhancing Network Security in IoT Applications through DDoS Attack Detection Using ML

Authors

Ahmed Mohamed Salama, Electronics and Communication Engineering Department Faculty of Engineering, Mansoura UniversityFollow
Mohamed AbdElAzim Mohamed, ELECTRONICS AND COMMUNICATION ENGINEERING Editor in - chief, Dean of the College, and Chairman of the Board of Directors Faculty of Engineering, Mansoura University, Egypt
Eman AbdElhalim, ELECTRONICS AND COMMUNICATIONS ENGINEERING Assistant Professor at the Department of Electronics & Communications Engineering Faculty of Engineering, Mansoura University, Egypt

Corresponding Author

Ahmed Mohamed Salama

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

A significant issue that affects contemporary network infrastructures is the Distributed Denial of Service (DDoS) attack, which presents serious dangers to organizations, people, and even governments. By flooding a target server, network, or website with deceptive traffic, this kind of cyberattack seeks to prevent it from providing services to legitimate users. For those in charge of maintaining network security, the prevalence and sophistication of these attacks have both grown significantly. DDoS attacks have the potential to lead to significant financial losses and service interruptions. Anomaly-based systems, traffic filtering, and Machine Learning (ML) algorithms are employed to spot them and lessen the effects of their influence. To successfully defend against DDoS attacks, it's imperative to have a proactive attitude and keep up with new security risks. In this study, the CICDDoS 2019 dataset was used to train and evaluate different ML algorithms, including Stochastic Gradient Boosting (SGB), Decision Tree (DT), K Nearest Neighbour (K-NN), Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR). The results showed that all the ML algorithms effectively detected DDoS attacks with high accuracy, precision, and recall. However, the SVM algorithm outperformed the other techniques, achieving the highest accuracy =0.99%.

Keywords

Distributed Denial of Service (DDoS), Machine Learning (ML), Stochastic Gradient Boosting (SGB), K nearest neighbor (K-NN), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Internet of Things (IoT).

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Salama, Ahmed Mohamed; Mohamed, Mohamed AbdElAzim; and AbdElhalim, Eman (2024) "Enhancing Network Security in IoT Applications through DDoS Attack Detection Using ML," Mansoura Engineering Journal: Vol. 49 : Iss. 3 , Article 10.
Available at: https://doi.org/10.58491/2735-4202.3181