A Security System for Detecting Denial of Service (DDoS) and Masquerade Attacks on Social Networks

Abstract

This study on a security system for detecting denial of service (DDoS) and masquerade attacks on social networks specifically describes how a Convolutional Neural Network (CNN) algorithm was employed. The dataset used for this research is the CICIDS2017 dataset, which contains benign data (no attack present) and the most up-to-date, frequent attacks which resemble true, real-world data. The feature extraction method used was recursive feature elimination (RFE), which reduced 77 columns of the dataset to 10 columns. This research was motivated by the limitation of Alguliyev and Abdullayeva 2019, which focused on the prediction of DDoS attack occurrence by getting related texts in social media. It has a limited attack class that focuses solely on DDoS attacks, and it does not perform social media network prediction in general. The objective of this research is to develop a security system for detecting DDoS and masquerade attacks and evaluate the detection model on social media networks. The system was tested on Facebook and Instagram. The result of the training accuracy that we derived from this research is 99.53%, while the testing accuracy is 99.52%. The result of this research is compared with previous studies’ results. This study recommends that the model implemented can be enhanced more effectively by comparing the accuracy of alternative deep learning algorithms to that of the CNN utilized in the current prediction model.