Optimasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Anomaly dengan Univariate Fitur Selection





Anomaly, Naïve Bayes, Univariate Selection


System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.


Alhakami, W., Alharbi, A., Bourouis, S., Alroobaea, R., & Bouguila, N. (2019). Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection. IEEE Access, 7, 52181–52190. https://doi.org/10.1109/ACCESS.2019.2912115

Anwar, S., Septian, F., & Septiana, R. D. (2019). Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 2(4), 135–140. https://doi.org/10.32493/jtsi.v2i4.3453

Arkaan, N., & Sakti, D. V. S. Y. (2019). Implementasi Low Interaction Honeypot Untuk Analisa Serangan Pada Protokol SSH. Jurnal Nasional Teknologi Dan Sistem Informasi, 5(2), 112–120. https://doi.org/10.25077/teknosi.v5i2.2019.112-120

Han, W., Xue, J., & Yan, H. (2019). Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine. IET Information Security, 13(2), 109–116. https://doi.org/10.1049/iet-ifs.2018.5186

Handayanna, F., Rinawati, Arisawati, E., & Dewi, L. S. (2017). Prediksi Penyakit Diabetes Menggunakan Naive Bayes Dengan Optimasi Parameter Menggunakan Algoritma Genetika. KNiST (Konferensi Nasional Ilmu Sosial & Teknologi), 71–76.

Karczmarek, P., Kiersztyn, A., Pedrycz, W., & Al, E. (2020). K-Means-based isolation forest. Knowledge-Based Systems, 195, 105659–105673. https://doi.org/10.1016/j.knosys.2020.105659

Koeswara, T. S. N., Mardiyanto, M. S., & Ghani, M. A. (2020). Penerapan Particle Swarm Optimization (Pso) Dalam Pemilihan Atribut Untuk Meningkatkan Akurasi Prediksi Diagnosispenyakit Hepatitis Dengan Metode Naive Bayes. Journal Speed – Sentra Penelitian Engineering Dan Edukasi, 12(1), 1–10.

Nachman, B., & Shih, D. (2020). Anomaly detection with density estimation. Physical Review D, 101(7), 075042–075057. https://doi.org/10.1103/PhysRevD.101.075042

Rahmansyah, A., Dewi, O., Andini, P., Hastuti, T., Ningrum, P., & Suryana, M. E. (2018). Membandingkan Pengaruh Feature Selection Terhadap Algoritma Naïve Bayes dan Support Vector Machine. Seminar Nasional Aplikasi Teknologi Informasi (SNATi), 1–7.

Ramdhani, Y., Susanti, S., Adiwisastra, M. F., & Topiq, S. (2018). Penerapan Algoritma Neural Network Untuk Klasifikasi Kardiotokografi. 5(1), 43–49.

Riadi, I., Umar, R., & Aini, F. D. (2019). Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm). ILKOM Jurnal Ilmiah, 11(1), 17–24. https://doi.org/10.33096/ilkom.v11i1.361.17-24

Ridho, F., & Kusuma, A. A. (2019). Deteksi Intrusi Jaringan dengan K-Means Clustering pada Akses Log dengan Teknik Pengolahan Big Data. Jurnal Aplikasi Statistika & Komputasi Statistik, 10(1), 53. https://doi.org/10.34123/jurnalasks.v10i1.202

Samponu, Y. B., & Kusrini, K. (2018). Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu. Jurnal ELTIKOM, 1(2), 56–63. https://doi.org/10.31961/eltikom.v1i2.29

Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., & Mueller, A. (2015). Scikit-learn. GetMobile: Mobile Computing and Communications, 19(1), 29–33.

Zhen, R., Jin, Y., Hu, Q., Shao, Z., & Nikitakos, N. (2017). Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier. Journal of Navigation, 70(3), 648–670.