Work place: School of Computer Science and Technology, Xidian University
E-mail: aflatoon353@gmail.com
Website:
Research Interests: Network Security, Machine Learning
Biography
Bassam M. Kanber is currently studying Master’s degree in school of Computer Science and Technology, Xidian University, Xi’an, China. He received a Bachelor of Science in Software Engineering, Taiz University, Taiz, Yemen. His research interest includes applications of Machine Learning and Network Security.
By Bassam M. Kanber Naglaa F. Noaman Amr M. H. Saeed Mansoor Malas
DOI: https://doi.org/10.5815/ijcnis.2022.03.03, Pub. Date: 8 Jun. 2022
Distributed Denial of Service (DDoS) is an ever-changing type of attack in cybersecurity, especially with the growing demand for cloud and web services raising a never-ending challenge in the lucrative business. DDoS attacks disrupt users' access to the targeted online services leading to significant business loss. This article presents a three-level architecture for detecting DDoS attacks at the application layer. The first level is responsible for selecting the best features of the samples and classifying the traffic into either benign or malicious, then the second level consists of a hard voting classifier to identify the type of the DDoS source: UDP, TCP, or Mixed-based. Finally, the last level aligns the attack to the appropriate DDoS type. This approach is validated using the CIC-DDoS2019 dataset, and the time, accuracy score, and precision are used as the model performance metrics. Compared to the existing machine learning (ML) approaches, the proposed architecture reveals substantial improvements in both binary and multiclass classification of application-layer DDoS attacks.
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