A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning

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A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning

Authors
  • Tekleselassie, Hailye
Type
Published Article
Journal
MATEC Web of Conferences
Publisher
EDP Sciences
Publication Date
Nov 17, 2021
Volume
348
Identifiers
DOI: 10.1051/matecconf/202134801012
Source
EDP Sciences
Keywords
License
Green
External links

Abstract

This research presents a novel combined learning method for developing a novel DDoS model that is expandable and flexible property of deep learning. This method can advance the current practice and problems in DDoS detection. A combined method of deep learning with knowledge-graph classification is proposed for DDoS detection. Whereas deep learning algorithm is used to develop a classifier model, knowledge-graph system makes the model expandable and flexible. It is analytically verified with CICIDS2017 dataset of 53.127 entire occurrences, by using ten-fold cross validation. Experimental outcome indicates that 99.97% performance is registered after connection. Fascinatingly, significant knowledge ironic learning for DDoS detection varies as a basic behavior of DDoS detection and prevention methods. So, security professionals are suggested to mix DDoS detection in their internet and network.

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