Development and evaluation of a network intrusion detection system for DDoS attack detection using machine learning

Abstract

Distributed denial of service (DDoS) attacks involves disrupting a target system by flooding it with an immense volume of traffic originating from numerous sources. These attacks can disrupt online services, causing financial losses and reputational damage to various organizations. To combat this threat, the proposed network intrusion detection system (NIDS) utilizes machine learning (ML) algorithms trained on the KDDCup99 dataset. This dataset encompasses a diverse array of network traffic patterns, bounded by both regular traffic and various attack types. By training the NIDS on this dataset, it becomes capable of accurately identifying DDoS attacks based on their distinctive patterns. The NIDS model is constructed using ML approaches like random forest (RF), support vector machines (SVM), and naive Bayes (NB). The developed NIDS is evaluated using performance metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curve. The proposed method demonstrates the NIDS’s accuracy of about 93%, precision of 99% and recall of 92% in detecting DDoS attacks, transforming it into a valuable tool for network security in comparison with the current methods. The study contributes to the domain of network security by providing an effective NIDS solution for detecting the DDoS attacks in the wireless sensor network.