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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Bibliographic Details
Published in:Sustainability 2021-11, Vol.13 (22), p.12514
Main Authors: Hsieh, Chih-Hsiang, Wang, Wei-Kuan, Wang, Cheng-Xun, Tsai, Shi-Chun, Lin, Yi-Bing
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bandwidths
Behavior
Classification
Computer architecture
Deep learning
Defense mechanisms
Denial of service attacks
Flooding
Internet of Things
Intrusion detection systems
Machine learning
Reaction time
Rerouteing
Security management
Software
Software-defined networking
Traffic
Traffic congestion
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su132212514