A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning

Department of Information Systems, School of Informatics, Wolaita Sodo University, Wolaita Sodo, Ethiopia

Correspondence should be addressed to Hailye Tekleselassie; hailyie.tekleselase@wsu.edu.et

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.