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An Intelligent Model for DDoS Attack Detection and Flash Event Management

An Intelligent Model for DDoS Attack Detection and Flash Event Management

Oreoluwa Carolyn Tinubu, Adesina Simon Sodiya, Olusegun Ayodeji Ojesanmi, Emmanuel Oyeyemi Adeleke, Ahmad Alfawwaz Timehin
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 15
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781683183471|DOI: 10.4018/IJDAI.301212
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MLA

Tinubu, Oreoluwa Carolyn, et al. "An Intelligent Model for DDoS Attack Detection and Flash Event Management." IJDAI vol.14, no.1 2022: pp.1-15. http://doi.org/10.4018/IJDAI.301212

APA

Tinubu, O. C., Sodiya, A. S., Ojesanmi, O. A., Adeleke, E. O., & Timehin, A. A. (2022). An Intelligent Model for DDoS Attack Detection and Flash Event Management. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-15. http://doi.org/10.4018/IJDAI.301212

Chicago

Tinubu, Oreoluwa Carolyn, et al. "An Intelligent Model for DDoS Attack Detection and Flash Event Management," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-15. http://doi.org/10.4018/IJDAI.301212

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Abstract

Distributed Denial of Service (DDoS) attacks are the foremost security concerns on the Internet. DDoS attacks and a similar occurrence called Flash Event (FE) signify anomalies in the normal network traffic, requiring intelligent interventions. This study presents the design and implementation of an intelligent model for the detection of application-layer DDoS attacks and the prevention of service degradations during FE. A Multi-Layer Perceptron (MLP) classifier was used for detecting DDoS attacks on application servers. The FE management system consists of asynchronous processing of requests on a First-In, First-Out (FIFO) basis. A demo application was set up wherein HTTP flood attack was launched and a Flash Event was simulated. The experimental results clearly show that the MLP classifier in comparison with other machine learning classifiers performs best in terms of speed and accuracy. Also, the evaluation of the FE management system shows a great reduction in service degradation. This reflects that the designed model is capable of averting service unavailability on the web.

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