Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset

Can, Duy Cat (2020) Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset. Technical Report. ACIIDS 2021. (Submitted)

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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:	Technical Report (Technical Report)
Subjects:	Information Technology (IT)
Divisions:	Faculty of Information Technology (FIT)
Depositing User:	Duy-Cat Can
Date Deposited:	14 Dec 2020 07:27
Last Modified:	14 Dec 2020 07:36
URI:	http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4266

Available Versions of this Item

• Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset. (deposited 14 Dec 2020 07:27) [Currently Displayed]
  • Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset. (deposited 18 Dec 2020 09:07)

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