Comparative Analysis of K-Nearest Neighbor and Decision Tree in Detecting Distributed Denial of Service

Distributed Denial of Service (DDoS) attacks are attacks made by several attackers by flooding the victim's device with a packet. The ease of making DDoS attacks has led to an increase of these attacks in network traffic. In contrast, the method of non-machine learning Intrusion Detection System (IDS) is now seen very inaccurate. There is a need then for an IDS method with machine learning (ML) that is more accurate in detecting attacks. Several previous studies have known that the K-Nearest Neighbor (KNN) and Decision Tree (DT) algorithms are two algorithms with high accuracy in detecting DDoS attacks. However, research comparing the two algorithms is not found so far. In this study, a comparative analysis was carried out between the two algorithms. The result of this study showed that DT had a higher accuracy with an accuracy value of 99.91% than KNN which only had an accuracy value of 98.94% in detecting DDoS attacks.