Intrusion Detection System Berbasis Seleksi Fitur Dengan Kombinasi Filter Information Gain Ratio Dan Correlation

Penulis

Nitami Lestari Putri, Radityo Adi Nugroho, Rudy Herteno

Abstrak

Intrusion Detection System merupakan suatu sistem yang dikembangkan untuk memantau dan memfilter aktivitas jaringan dengan mengidentifikasi serangan. Karena jumlah data yang perlu diperiksa oleh IDS sangat besar dan banyaknya fitur-fitur asing yang dapat membuat proses analisis menjadi sulit untuk mendeteksi pola perilaku yang mencurigakan, maka IDS perlu mengurangi jumlah data yang akan diproses dengan cara mengurangi fitur yang dapat dilakukan dengan seleksi fitur. Pada penelitian ini mengkombinasikan dua metode perangkingan fitur yaitu Information Gain Ratio dan Correlation dan mengklasifikasikannya menggunakan algoritma K-Nearest Neighbor. Hasil perankingan dari kedua metode dibagi menjadi dua kelompok. Pada kelompok pertama dicari nilai mediannya dan untuk kelompok kedua dihapus. Lalu dilakukan klasifikasi K-Nearest Neighbor dengan menggunakan 10 kali validasi silang dan dilakukan pengujian dengan nilai k=5. Penerapan pemodelan yang diusulkan menghasilkan akurasi tertinggi sebesar 99.61%. Sedangkan untuk akurasi tanpa seleksi fitur menghasilkan akurasi tertinggi sebesar 99.59%.

 

Abstract

Intrusion Detection System is a system that was developed for monitoring and filtering activity in network with identified of attack. Because of the amount of the data that need to be checked by IDS is very large and many foreign feature that can make the analysis process difficult for detection suspicious pattern of behavior, so that IDS need for reduce amount of the data to be processed by reducing features that can be done by feature selection. In this study, combines two methods of feature ranking is Information Gain Ratio and Correlation and classify it using K-Nearest Neighbor algorithm. The result of feature ranking from the both methods divided into two groups. in the first group searched for the median value and in the second group is removed. Then do the classification of  K-Nearest Neighbor using 10 fold cross validation and do the tests with values k=5. The result of the  proposed modelling produce the highest accuracy of 99.61%. While the highest accuracy value of the not using the feature selection is 99.59%.

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Referensi


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DOI: http://dx.doi.org/10.25126/jtiik.0813154