A Robust Pipeline Approach for DDoS Classification using Machine Learning

Abstract

Remote and edge devices have less security features that are easily exploited by hackers. The security of businesses in major domains depends on the security features the infrastructure has to offer. Major breaches have been reported over the past years which have led to compromise of hidden data. DDoS attacks have been a major trend which has brought down many devices using similar techniques. Major vulnerabilities have been found in IoT systems which presents an open door for hackers. To address the upcoming trends in early vulnerabilities detection, a standard predictive model of DDoS attacks needs to be implemented. In this paper we propose a robust pipeline for DDoS classification and the performance of the models are calculated against the metrics such as precision, recall and f1-scores. After evaluating various machine learning models, the XGboost algorithm works well on our data set with an accuracy score of 99% outperforming other models.