FEATURE DEPENDENT NAÏVE BAYES UNTUK NETWORK INTRUSION DETECTION SYSTEM

FEATURE DEPENDENT NAÏVE BAYES UNTUK NETWORK INTRUSION DETECTION SYSTEM

• By Rahayuningsih, Panny;
• Tesis
• 294
• 1-123

Intrusion detection system adalah komponen penting yang melakukan analisa untuk mendeteksi,mencengah serangan internet berbasis host atau berbasis jaringan. masalah yang timbul dari IDS adalah banyaknya kumpulan data dalam jaringan sistem komputer sehingga mengakibatkan akurasi deteksi menjadi rendah. untuk meningkatkan akurasi menjadi tinggi dan tingkat false positive

rendah pendekatan dengan machine learning di terapkan. Algoritma data mining klasifikasi yang digunakan adalah Naïve bayes salah satu algoritma yang banyak digunakan dalam klasifikasi karena kesederhaan, efisiensi dan efektivitasnya. NB memiliki akurasi dan kecepatan yang tinggi saat diaplikasikan ke dalam database dengan data yang besar. Namun, algoritma NB mengasumsikan atribut independen (bebas) serta sangat sensitif terhadap seleksi fitur yang banyak sehingga mengganggu kinerja atau akurasi NB menjadi rendah tapi dalam praktiknya, kemungkinan fitur saling terkait. Metode Feature Dependent Naïve Bayes (FDNB) adalah metode yang efektif digunakan untuk memecahkan masalah yang ada di NB dengan menghitung fitur sebagai pasangan dan menciptakan ketergantungan antara satu sama lain serta dengan menerapkan learning model diimplementasikan ke cross-validation, Feature Selection dan langkah-langkah data preprocessing yang memberikan hasil akurasi lebih baik. Setelah dilakukan pengujian dengan dua model yaitu Naïve bayes dan FDNB maka hasil yang didapatkan dari algoritma Naïve Bayes menghasilkan akurasi sebesar 84.42%, sedangkan untuk FDNB dan oversampling (CFS+GS) nilai akurasinya sebesar 94.58%, FDNB dan oversampling (CFS+BFS) nilai akurasinya sebesar 94.69%, FDNB dan SMOTE (CFS+GS) FDNB dan SMOTE (CFS+BFS) nilai akurasinya sebesar 93,56%. Untuk rata-rata per attack type serangan Dos menunjukkan hasil yang paling tinggi untuk nilai akurasinya sebesar 97.86% dan serangan U2R menghasilkan akurasi terbaik saat mengklasifikasikan U2R dengan akurasi 93.80%, ukuran F-mea U2R sebesar 96,63% dapat dianggap sebagai hasil yang sangat bagus. Karena serangan U2R dianggap sangat berbahaya

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