Boosting Algorithms to Identify Distributed Denial-of-Service Attacks

Abstract

Abstract In the current pandemic situation, much work became automated using Internet of Things (IoT) devices. The security of IoT devices is a major issue because they can easily be hacked by third parties. Attackers cause interruptions in vital ongoing operations through these hacked devices. Thus, the demand for an efficient attack identification system has increased in the last few years. The present research aims to identify modern distributed denial-of-service (DDoS) attacks. To provide a solution to the problem of DDoS attacks, an openly available dataset (CICDDoS 2019) has recently been introduced and implemented. The attacks currently occurring in the dataset were identified using two machine learning methods, i.e. the light gradient boosting method (LGBM) and extreme gradient boosting (XGBoost). These methods have been selected because of their superior prediction ability in high volumes of data in less time than other methods require. The accuracy achieved by LGBM and XGBoost were 94.88% and 94.89% in 30 and 229 seconds(s), respectively.