Efficient Detection of Link-Flooding Attacks with Deep Learning

Chih-Hsiang Hsieh, Wei-Kuan Wang, Cheng-Xun Wang, Shi-Chun Tsai and Yi-Bing Lin
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Chih-Hsiang Hsieh: Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Wei-Kuan Wang: Institute of Network Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Cheng-Xun Wang: Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Shi-Chun Tsai: Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Yi-Bing Lin: Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

Sustainability, 2021, vol. 13, issue 22, 1-11

Abstract: 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.

Keywords: distributed denial of service (DDoS) attack; link-flooding attack (LFA); deep learning (DL); software defined networking (SDN) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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