An Intelligent Model for DDoS Attack Detection and Flash Event Management

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.

Isolation of DDoS Attacks and Flash Events in Internet Traffic Using Deep Learning Techniques

“…The contributions were significant nevertheless the techniques could not identify FEs. The other studies as reported by ), Sun et al, (2019, and Tinubu et al (2022) proposed detection models that used the KOAD algorithm, KNN, and A Multi-Layer Perceptron (MLP) classifier respectively. Of the reviewed techniques, the authors attempted to develop models that could isolate the occurrence of either DDoS and/or FE whenever subjected to a specific attack.…”