A Comparative Study Of Binary Class Logistic Regression and Shallow Neural Network For DDoS Attack Prediction - Tufail, Shahid; Batool, Shanzeh; Sarwat, Arif I. (Proceedings of IEEE Southeastcon, 2022)
In the area of internet security, cybersecurity is a serious subject. Every industry is witnessing thousands of cyberattacks every year. Among the most deadly cyber-attacks are the distributed denial-of-service attack (DDOS) and the False data injection attack (FDIA). In this paper, we performed a comparative study for predicting DDOS attacks using two machine learning algorithms that are logistic regression and shallow neural network(SNN). In logistic regression, we achieved an accuracy of 98.63% and for SNN accuracy we achieved was 99.85%. However, our study shows that the training time was exponentially higher for SNN in comparison to logistic regression.