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Reciprocal Points Learning Based Unknown DDoS Attacks Detection

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

Abstract

In recent years, the increasing reliance on Internet services has made the Internet an integral part of our daily life. The COVID-19 pandemic has further accelerated this trend by driving the demand for online services such as remote work, virtual meetings, and online events. However, this increasing dependence on the Internet has also made us vulnerable to various cyber threats, particularly DDoS attacks, which have become a serious issue. For this reason, researchers have proposed numerous defense mechanisms to mitigate the risks associated with DDoS attacks, among which Machine Learning (ML) based Intrusion Detection Systems (IDS) have shown promising results. Nevertheless, most existing ML-based IDSs focus on known attack features, leaving them vulnerable to attacks that utilize unknown features. To overcome this limitation, researchers propose a new concept Open-Set Recognition (OSR), which explores new approaches that modify the Deep Learning method to identify unknown patterns. Therefore, we propose a novel IDS model based on OSR to detect Unknown DDoS attacks. The model detects unknown DDoS attacks with the U-Net + Reciprocal Points Learning (RPL). With a detection rate of approximately 99%, our model can successfully identify known and unknown DDoS attacks while maintaining an ability to manage imbalanced situations.

This work was supported by National Science and Technology Council, Taiwan, grant No. MOST 111-2221-E-992-066- and MOST 109-2221-E-992-073-MY3.

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Correspondence to Fu-An Ho .

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Ho, FA., Shieh, CS., Horng, MF., Nguyen, TT., Chao, YC. (2023). Reciprocal Points Learning Based Unknown DDoS Attacks Detection. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_7

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  • DOI: https://doi.org/10.1007/978-3-031-42430-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42429-8

  • Online ISBN: 978-3-031-42430-4

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