Predicting network traffic anomalies in Denial-of- service attacks – a nonlinear approach

Ding, Wei Lau and Po, Hung Lai and Yu, Beng Leau and Soo, Fun Tan (2021) Predicting network traffic anomalies in Denial-of- service attacks – a nonlinear approach.

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Abstract

The amount of data moving across the network at any given time is referred to as network traffic. It is the data units that are encapsulated in packets and sent over a network. Denial-of-Service (DDoS) attacks are various attempts to disrupt typical network, service, or server traffic. DDoS attacks attempt to disrupt legitimate users' work and data transfers by sending large packets or traffic. Various network traffic prediction techniques are investigated in this study, and a nonlinear time series method, Multilayer Perceptron Neural Network (MLPNN), has been chosen to evaluate network traffic prediction. The results with the NSL-KDD dataset show that the approach can improve prediction accuracy by up to 98.87%. With 2.26%, it outperforms other models such as Sequential Minimal Optimization (SMO).

Item Type:	Proceedings
Keyword:	Multilayer Perceptron Neural Network , Nonlinear Time Series , Network Traffic , Network Traffic Prediction , Prediction Accuracy
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	03 May 2022 21:41
Last Modified:	03 May 2022 21:41
URI:	https://eprints.ums.edu.my/id/eprint/32531

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