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Eye-Net: A Low-Complexity Distributed Denial of Service Attack-Detection System Based on Multilayer Perceptron

Authors :
Ramzi Khantouchi
Ibtissem Gasmi
Mohamed Amine Ferrag
Source :
Journal of Sensor and Actuator Networks, Vol 13, Iss 4, p 45 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Distributed Denial of Service (DDoS) attacks disrupt service availability, leading to significant financial setbacks for individuals and businesses. This paper introduces Eye-Net, a deep learning-based system optimized for DDoS attack detection that combines feature selection, balancing methods, Multilayer Perceptron (MLP), and quantization-aware training (QAT) techniques. An Analysis of Variance (ANOVA) algorithm is initially applied to the dataset to identify the most distinctive features. Subsequently, the Synthetic Minority Oversampling Technique (SMOTE) balances the dataset by augmenting samples for under-represented classes. Two distinct MLP models are developed: one for the binary classification of flow packets as regular or DDoS traffic and another for identifying six specific DDoS attack types. We store MLP model weights at 8-bit precision by incorporating the quantization-aware training technique. This adjustment slashes memory use by a factor of four and reduces computational cost similarly, making Eye-Net suitable for Internet of Things (IoT) devices. Both models are rigorously trained and assessed using the CICDDoS2019 dataset. Test results reveal that Eye-Net excels, surpassing contemporary DDoS detection techniques in accuracy, recall, precision, and F1 Score. The multiclass model achieves an impressive accuracy of 96.47% with an error rate of 8.78%, while the binary model showcases an outstanding 99.99% accuracy, maintaining a negligible error rate of 0.02%.

Details

Language :
English
ISSN :
22242708
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Sensor and Actuator Networks
Publication Type :
Academic Journal
Accession number :
edsdoj.6a3e900a47f297de81c056e25f13
Document Type :
article
Full Text :
https://doi.org/10.3390/jsan13040045