DoS attack detection using Aquila deer hunting optimization enabled deep belief network

Abstract

Purpose

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

Design/methodology/approach

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

Findings

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

Originality/value

The introduced detection approach effectively detects DoS attacks available on the internet.

Citation

Thomas, M. and B.B., M. (2024), "DoS attack detection using Aquila deer hunting optimization enabled deep belief network", International Journal of Web Information Systems, Vol. 20 No. 1, pp. 66-87. https://doi.org/10.1108/IJWIS-06-2023-0089