Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric

Chin-Shiuh Shieh, Thanh-Tuan Nguyen () and Mong-Fong Horng ()
Additional contact information
Chin-Shiuh Shieh: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Thanh-Tuan Nguyen: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Mong-Fong Horng: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan

Mathematics, 2023, vol. 11, issue 9, 1-24

Abstract: DDoS attacks remain a persistent cybersecurity threat, blocking services to legitimate users and causing significant damage to reputation, finances, and potential customers. For the detection of DDoS attacks, machine learning techniques such as supervised learning have been extensively employed, but their effectiveness declines when the framework confronts patterns exterior to the dataset. In addition, DDoS attack schemes continue to improve, rendering conventional data model-based training ineffectual. We have developed a novelty open-set recognition framework for DDoS attack detection to overcome the challenges of traditional methods. Our framework is built on a Convolutional Neural Network (CNN) construction featuring geometrical metric (CNN-Geo), which utilizes deep learning techniques to enhance accuracy. In addition, we have integrated an incremental learning module that can efficiently incorporate novel unknown traffic identified by telecommunication experts through the monitoring process. This unique approach provides an effective solution for identifying and alleviating DDoS. The module continuously improves the model’s performance by incorporating new knowledge and adapting to new attack patterns. The proposed model can detect unknown DDoS attacks with a detection rate of over 99% on conventional attacks from CICIDS2017. The model’s accuracy is further enhanced by 99.8% toward unknown attacks with the open datasets CICDDoS2019.

Keywords: cybersecurity; distributed denial-of-service (DDoS); convolutional neural networks (CNN); geometrical metric; incremental learning; open-set recognition (OSR); machine learning; deep learning; unknown attack; CICIDS2017; CICDDoS2019 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/9/2145/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/9/2145/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:9:p:2145-:d:1138628

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().