Analisa Performa Penggunaan Feature Selection untuk Mendeteksi Intrusion Detection Systems dengan Algoritma Random Forest Classifier

Setiawan Budiman, Andi Sunyoto, Asro Nasiri



Semakin penting koneksi data melalui Internet membuat kebutuhan akan keamanan jaringan data semakin meningkat. Salah satu tools yang penting adalah Intrusion detection systems (IDS). Salah satu hal yang menjadi masalah dari penggunaan IDS adalah performan kecepatan untuk mendeteksi data yang semakin banyak dalam waktu yang singkat. Dalam penelitian ini kami akan melakukan analisa perbandingan performa IDS menggunakan features selection dengan algoritma Random Forest Classifier yang disimulasikan pada dataset UNSW-NB15, yaitu dataset simulasi serangan pada jaringan network yang dikembangan oleh Nour Moustafa & Jill Slay dari University of New South Wales pada Australian Defence Force Academy. Tujuan dari penelitian ini adalah mempercepat waktu proses Intrusion detection systems dengan machile learning. Penelitian dilakukan dengan 2 tahap, yaitu tahap pertama tanpa features selection dan tahap kedua dengan features selection ExtraTreesClassifier. Masing-masing tahap dilakukan dengan beberapa kali pengujian dengan persentasi testing dan training data yang berbeda. Hasil penelitian menunjukan bahwa penggunaan features selection dapat mempercepat waktu proses pendeteksian dengan menggunakan Random Forest Classifier, walaupun ada sedikit penurun akurasi dibawah 1%.

Kata kunci: feature selection, random forest, ids, machine learning



Internet data connection is very important, therefore it will increasing the security issues. One of the important tools is Intrusion detection systems (IDS). The main problems of using IDS is the speed performance to detect more and more data in a short time. In this study, we will perform a comparative analysis of IDS performance using features selection with the Random Forest Classifier algorithm which is simulated on the UNSW-NB15 dataset, which is work as the attack simulation dataset on the network developed by Nour Moustafa & Jill Slay from the University of New South Wales at the Australian Defense Force Academy. The purpose of this research is to speed up the processing time of Intrusion detection systems with machile learning. The research was conducted in 2 stages, the first stage without features selection and the second stage with features selection. Each stage is carried out with several study using different percentages of testing and training data. The results showed that by using features selection, it can speed up the detection process time using the Random Forest Classifier, although there is a slight decrease in accuracy below 1%.

Keywords: feature selection, random forest, ids, machine learning

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