Intrusion Detection Systems pada Bot-IoT Dataset Menggunakan Algoritma Machine Learning

Abstract

As technological advancement grows, so is the daily usage of internet. The growing usage of internet raises concerns about security safety when using services on the internet. To ensure user security, the Intrusion Detection System (IDS) can be used. Intrusion Detection System is a system that will monitor activities in a computer network using various methods such as machine learning. In this research journal, three kinds of machine learning algorithms are used to assist IDS in recognizing attacks. The machine learning algorithms used are K-Nearest Neighbor, Random Forest, and Gaussian Naïve Bayes. To assist the research, the BoT-IoT Dataset created by UNSW Canberra was also used by taking 5% of the entire dataset. This research was conducted with the aim of determining the most suitable algorithm in performing intrusion detection with the BoT-IoT dataset.