Efficient Detection of Link-Flooding Attacks with Deep Learning

The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks,...

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Autores principales:	Chih-Hsiang Hsieh, Wei-Kuan Wang, Cheng-Xun Wang, Shi-Chun Tsai, Yi-Bing Lin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	distributed denial of service (DDoS) attack link-flooding attack (LFA) deep learning (DL) software defined networking (SDN) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ea2ce610c8014a9daddb9f4c262e50d1
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id	oai:doaj.org-article:ea2ce610c8014a9daddb9f4c262e50d1
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spelling	oai:doaj.org-article:ea2ce610c8014a9daddb9f4c262e50d12021-11-25T19:01:40ZEfficient Detection of Link-Flooding Attacks with Deep Learning10.3390/su1322125142071-1050https://doaj.org/article/ea2ce610c8014a9daddb9f4c262e50d12021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12514https://doaj.org/toc/2071-1050The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and implement our ideas with a deep learning approach to improve the performance of the previous work. Through rerouting techniques and monitoring network traffic, our system can detect a malicious attack from the adversary. A CNN architecture is combined to assist in finding an appropriate rerouting path that can shorten the reaction time for detecting DDoS attacks. Therefore, the proposed method can efficiently distinguish the difference between benign traffic and malicious traffic and prevent attackers from carrying out link-flooding attacks through bots.Chih-Hsiang HsiehWei-Kuan WangCheng-Xun WangShi-Chun TsaiYi-Bing LinMDPI AGarticledistributed denial of service (DDoS) attacklink-flooding attack (LFA)deep learning (DL)software defined networking (SDN)Environmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12514, p 12514 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	distributed denial of service (DDoS) attack link-flooding attack (LFA) deep learning (DL) software defined networking (SDN) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	distributed denial of service (DDoS) attack link-flooding attack (LFA) deep learning (DL) software defined networking (SDN) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Chih-Hsiang Hsieh Wei-Kuan Wang Cheng-Xun Wang Shi-Chun Tsai Yi-Bing Lin Efficient Detection of Link-Flooding Attacks with Deep Learning
description	The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and implement our ideas with a deep learning approach to improve the performance of the previous work. Through rerouting techniques and monitoring network traffic, our system can detect a malicious attack from the adversary. A CNN architecture is combined to assist in finding an appropriate rerouting path that can shorten the reaction time for detecting DDoS attacks. Therefore, the proposed method can efficiently distinguish the difference between benign traffic and malicious traffic and prevent attackers from carrying out link-flooding attacks through bots.
format	article
author	Chih-Hsiang Hsieh Wei-Kuan Wang Cheng-Xun Wang Shi-Chun Tsai Yi-Bing Lin
author_facet	Chih-Hsiang Hsieh Wei-Kuan Wang Cheng-Xun Wang Shi-Chun Tsai Yi-Bing Lin
author_sort	Chih-Hsiang Hsieh
title	Efficient Detection of Link-Flooding Attacks with Deep Learning
title_short	Efficient Detection of Link-Flooding Attacks with Deep Learning
title_full	Efficient Detection of Link-Flooding Attacks with Deep Learning
title_fullStr	Efficient Detection of Link-Flooding Attacks with Deep Learning
title_full_unstemmed	Efficient Detection of Link-Flooding Attacks with Deep Learning
title_sort	efficient detection of link-flooding attacks with deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ea2ce610c8014a9daddb9f4c262e50d1
work_keys_str_mv	AT chihhsianghsieh efficientdetectionoflinkfloodingattackswithdeeplearning AT weikuanwang efficientdetectionoflinkfloodingattackswithdeeplearning AT chengxunwang efficientdetectionoflinkfloodingattackswithdeeplearning AT shichuntsai efficientdetectionoflinkfloodingattackswithdeeplearning AT yibinglin efficientdetectionoflinkfloodingattackswithdeeplearning
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Efficient Detection of Link-Flooding Attacks with Deep Learning

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