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

Jonathan Nicholas Sibarani; Dheo Ronaldo Sirait; Salma Safira Ramadhanti

doi:10.14710/jmasif.14.1.49721

DOI: https://doi.org/10.14710/jmasif.14.1.49721

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

Jonathan Nicholas Sibarani¹ , Dheo Ronaldo Sirait², Salma Safira Ramadhanti²

¹Departemen Informatika, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

²Universitas Diponegoro, Indonesia

Received: 20 Oct 2022; Revised: 16 Jun 2023; Accepted: 16 Jun 2023; Available online: 21 Jun 2023; Published: 21 Jun 2023.

Editor(s): Kabul Kurniawan

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Semakin berkembangnya dunia teknologi, semakin banyak juga penggunaan internet dalam kehidupan sehari hari. Pertumbuhan dalam penggunaan internet tersebut menimbulkan kekhawatiran tentang keamanan saat menggunakan layanan internet. Untuk menjamin keamanan pengguna, dapat menggunakan Intrusion Detection System (IDS). Intrusion Detection System merupakan sebuah sistem yang akan mengawasi aktivitas dalam jaringan komputer dengan menggunakan berbagai macam metode seperti machine learning. Dalam jurnal penelitian ini, digunakan tiga macam algoritma machine learning untuk membantu IDS dalam mengenali serangan. Algoritma machine learning yang digunakan adalah K-Nearest Neighbor, Random Forest, dan Gaussian Naïve Bayes. Untuk membantu penelitian juga digunakan BoT-IoT Dataset yang dibuat oleh UNSW Canberra dengan lebih dari 72.000.000 baris data. Penelitian ini dilakukan dengan tujuan untuk menentukan algoritma yang paling sesuai dalam melakukan deteksi intrusi dengan dataset BoT-IoT.

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Keywords: K-Nearest Neighbor; Random Forest; Gaussian Naive Bayes; Intrusion Detection System; Machine Learning; Cybersecurity

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Section: Research Article

Language : ID

In Vol 14, No 1 (2023): JURNAL MASYARAKAT INFORMATIKA

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