Can, Duy Cat and Le, Hoang Quynh and Ha, Quang Thuy (2021) Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset. In: ACIIDS-2021. (In Press)
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Abstract
Abstract: With the development of technology, the highly accessible internet service is the biggest demand for most people. Online network, however, has been suffering from malicious attempts to disrupt essential web technologies, resulting in service failures. In this work, we introduced a model to detect and classify Distributed Denial of Service attacks based on neural networks that take advantage of a proposed automatic feature selection component. The experimental results on CIC-DDoS 2019dataset have demonstrated that our proposed model outperformed other machine learning-based model by large margin. We also investigated the effectiveness of weighted loss and hinge loss on handling the class imbalance problem.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Information Technology (IT) |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Duy-Cat Can |
| Date Deposited: | 18 Dec 2020 09:07 |
| Last Modified: | 21 Dec 2020 09:18 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4289 |
Available Versions of this Item
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Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset. (deposited 14 Dec 2020 07:27)
- Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset. (deposited 18 Dec 2020 09:07) [Currently Displayed]
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