Abstract

Distributed Denial of Service (DDoS) is a server-side infrastructure type security attack that aims to prevent legitimate users from accessing server system resources. Huge financial losses, reputation damage and data theft are some of the serious circumstances of DDoS attacks. Available DDoS attack detection methods reduce the severity of the attack's consequences, but they require more data computation, which is more expensive. This research proposed two feature selection methods in order to reduce the data computation for TCP-based DDoS attack detection with Support Vector Machine (SVM) classification algorithm. The first feature selection proposal of this study is to use Pearson, Spearman, and Kendall correlation approaches to select the PSK common uncorrelated feature subset. Use these PSK common uncorrelated feature subsets with SVM classifier with different kernels on TCP-based DDoS attacks and evaluate the classification results. This research, performed operations on Syn flood, MSSQL, SSDP datasets have taken from the CIC-DDoS2019 evaluation dataset. Select TCP-based DDoS attacks common uncorrelated feature subset selected by applying intersection on Syn flood, MSSQL, and SSDP data sets PSK common uncorrelated feature subsets is the second feature selection proposal of this research. Use these TCP-based DDoS attacks common uncorrelated feature subsets with SVM classifier with different kernels on TCP-based DDoS attacks and evaluate the classification results. Results with these two proposed methods also compared in this study. Experiments have been performed with these two approaches on a customized TCP-based DDoS attack that's been developed with Syn flood, MSSQL, and SSDP data sets, and the results have been evaluated. Linear, rbf, poly, sigmoid kernels SVM kernels used in this research. Experiments conclude that SVM with rbf kernel produces better results on TCP-based DDoS attacks.

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