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All Journal Bulletin of Electrical Engineering and Informatics
Olaniyi Mathew Olayiwola
Federal University of Agriculture
Electrical & Electronics Engineering

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Efficient model for detecting application layer distributed denial of service attacks Morenikeji Kabirat Kareem; Olaniyi Dada Aborisade; Saidat Adebukola Onashoga; Tole Sutikno; Olaniyi Mathew Olayiwola
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3871

Abstract

The increasing advancement of technologies and communication infrastructures has been posing threats to the internet services. One of the most powerful attack weapons for disrupting web-based services is the distributed denial of service (DDoS) attack. The sophisticated nature of attack tools being created and used for launching attacks on target systems makes it difficult to distinguish between normal and attack traffic. Consequently, there is a need to detect application layer DDoS attacks from network traffic efficiently. This paper proposes a detection system coined eXtreme gradient boosting (XGB-DDoS) using a tree-based ensemble model known as XGBoost to detect application layer DDoS attacks. The Canadian institute for cybersecurity intrusion detection systems (CIC IDS) 2017 dataset consisting of both benign and malicious attacks was used in training and testing of the proposed model. The performance results of the proposed model indicate that the accuracy rate, recall, precision rate, and F1-score of XGB-DDoS are 0.999, 0.997, 0.995, and 0.996, respectively, as against those of k-nearest neighbor (KNN), support vector machine (SVM), principal component analysis (PCA) hybridized with XGBoost, and KNN with SVM. So, the XGB-DDoS detection model did better than the models that were chosen. This shows that it is good at finding application layer DDoS attacks.
Co-Authors Morenikeji Kabirat Kareem Olaniyi Dada Aborisade Saidat Adebukola Onashoga Tole Sutikno