Peningkatan Akurasi Prediksi pada Jaringan IoT terhadap Serangan DDOS dengan Kombinasi Algoritma Ripper dan Algoritma KNN

Abstract

The Internet of Things (IoT) encompasses a network of interconnected devices that communicate through the internet, integrating sensors, software, and other technologies. As the number of connected devices grows, ensuring IoT security has become increasingly critical, with the risk of cyber threats rising in tandem. A prevalent threat is the Distributed Denial of Service (DDoS) attack, which seeks to incapacitate server systems by overwhelming the network with excessive traffic. This study explores the use of machine learning techniques to enhance the accuracy of intrusion detection systems (IDS) in identifying and mitigating DDoS attacks. Specifically, it employs a hybrid approach combining the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm and the K-Nearest Neighbors (KNN) algorithm as detection models. The study utilizes the Network Security Layer - Knowledge Discovery in Database (NSL-KDD) dataset, which contains 41 features relevant to classifying DDoS attacks. To optimize the model’s performance, the data underwent preprocessing to eliminate irrelevant and redundant features while retaining essential information. The classification results demonstrate a peak accuracy of 97.32% with k=6, and an average accuracy of 96.30% across 15 trials with varying k values from 1 to 15. These findings highlight the effectiveness of the RIPPER-KNN approach in improving the prediction accuracy of DDoS attack detection in IoT networks.