AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potential...

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Autores principales:	Yuanyuan Wei, Julian Jang-Jaccard, Fariza Sabrina, Amardeep Singh, Wen Xu, Seyit Camtepe
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Distributed denial of service DDoS deep learning multi-class classification autoencoder MLP Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ac33e481129640ae8caa3ddace890695
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spelling	oai:doaj.org-article:ac33e481129640ae8caa3ddace8906952021-11-05T23:00:15ZAE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification2169-353610.1109/ACCESS.2021.3123791https://doaj.org/article/ac33e481129640ae8caa3ddace8906952021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591559/https://doaj.org/toc/2169-3536Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.Yuanyuan WeiJulian Jang-JaccardFariza SabrinaAmardeep SinghWen XuSeyit CamtepeIEEEarticleDistributed denial of serviceDDoSdeep learningmulti-class classificationautoencoderMLPElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146810-146821 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Distributed denial of service DDoS deep learning multi-class classification autoencoder MLP Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Distributed denial of service DDoS deep learning multi-class classification autoencoder MLP Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuanyuan Wei Julian Jang-Jaccard Fariza Sabrina Amardeep Singh Wen Xu Seyit Camtepe AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
description	Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.
format	article
author	Yuanyuan Wei Julian Jang-Jaccard Fariza Sabrina Amardeep Singh Wen Xu Seyit Camtepe
author_facet	Yuanyuan Wei Julian Jang-Jaccard Fariza Sabrina Amardeep Singh Wen Xu Seyit Camtepe
author_sort	Yuanyuan Wei
title	AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
title_short	AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
title_full	AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
title_fullStr	AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
title_full_unstemmed	AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
title_sort	ae-mlp: a hybrid deep learning approach for ddos detection and classification
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/ac33e481129640ae8caa3ddace890695
work_keys_str_mv	AT yuanyuanwei aemlpahybriddeeplearningapproachforddosdetectionandclassification AT julianjangjaccard aemlpahybriddeeplearningapproachforddosdetectionandclassification AT farizasabrina aemlpahybriddeeplearningapproachforddosdetectionandclassification AT amardeepsingh aemlpahybriddeeplearningapproachforddosdetectionandclassification AT wenxu aemlpahybriddeeplearningapproachforddosdetectionandclassification AT seyitcamtepe aemlpahybriddeeplearningapproachforddosdetectionandclassification
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AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

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