Syn Flood DDoS Attack Detection with Different Multilayer Perceptron Optimization Techniques Using Uncorrelated Feature Subsets Selected by Different Correlation Methods

Cyber attackers widely used Distributed Denial of Service (DDoS) attacks to saturate servers with network traffic, preventing authorized clients to access network resources and ensuing massive losses in all aspects of the organizations. With the use of ADAM, SGD, and LBFGS optimization techniques, this paper evaluates a Multilayer Perceptron (MLP) classification algorithm for Syn flood DDoS attack detection using various uncorrelated features chosen with Pearson, Spearman, and Kendall correlation methods. Dataset for a Syn flood DDoS attack was taken from the CIC-DDoS2019 dataset. Experiment results conclude that among optimization techniques, ADAM optimization gives better results and among uncorrelation feature sets and Pearson uncorrelated feature subset produce the best results. Multilayer Perceptron produces the best classification results with ADAM optimization and Pearson uncorrelation subset on Syn flood DDoS attack.