ANALISIS PERBANDINGAN DETECTION TRAFFIC ANOMALY DENGAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM)

Imam Riadi, Rusydi Umar, Fadhilah Dhinur Aini

Abstract


Intrusion Detection System (IDS) is a software or hardware that can be used to detect any abnormal activity in the network. Situations often arise from various network access in the form of information or data that can cause problems. Detection is a system for detecting activities that are disturbing data access in information. IDS has two methods of doing detection, namely Rule Based (Signature Based) and Behavior-Based. Anomaly traffic can detect an increase in the number of user access and at any time there will be an attack from another party on the network. This study uses 2 algorithm methods are Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes results through the Distributions and Radviz graph data samples have a probability value of 0.1 and the highest probability value is 0.8. Support Vector Machine (SVM) produces a graph that has greater accuracy.


Keywords


Classification Naive Bayes; Support Vector Machine (SVM); Intrusion Detection System (IDS); Traffic Anomaly

Full Text:

PDF

References


M. Jannah, Hustinawati, and R. Wildani, “Implementasi Intrusion System (Ids) Snort Pada Laboratorium Jaringan Komputer,” UG J., vol. 6 No 5, pp. 1–4, 2012.

M. Sudarma and D. P. Hostiadi, “Komunikasi Pada Nework Traffic Menggunakan Naïve Bayes Sebagai,” Icsgteis, no. November, pp. 59–64, 2013.

Y. Purwanto and F. Y. Suratman, “Perancangan Dan Analisis Deteksi Anomali Berbasis Clustering Menggunakan Algoritma Modified K-Means Dengan Timestamp Initialization Pada Sliding Window Design And Analysis Of Anomaly Detection Based Clustering Using Modified K-Means Algorithm With Timesta.”

E. Risyad, M. Data, and E. S. Pramukantoro, “Perbandingan Performa Intrusion Detection System ( IDS ) Snort Dan Suricata Dalam Mendeteksi Serangan TCP SYN Flood,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 9, pp. 2615–2624, 2018.

E. Susilowati, M. K. Sabariah, and A. A. Gozali, “Implementasi Metode Support Vector Machine Untuk Melakukan Klasifikasi Kemacetan Lalu Lintas Pada Twitter Implementation Support Vector Machine Method for Traffic Jam Classification on Twitter,” E-Proceeding Eng., vol. 2, no. 1, pp. 1–7, 2015.

A. P. Wicaksono, J. Raya, D. Po, and B. Purwokerto, “Sistem Deteksi Intrusi dengan Snort ( Intrusion Detection System with Snort ),” vol. III, pp. 31–34, 2014.

W. A. Mukti, S. U. Masruroh, D. Khairani, and B. Forensik, “Analisa Dan Perbandingan Bukti Forensik Aplikasi Media Sosial Facebook Dan Twitter Pada Smartphone Android,” vol. 10, no. 1, pp. 73–84, 2017.

M. N. Faiz, R. Umar, and A. Yudhana, “Analisis Live Forensics Untuk Perbandingan Kemananan Email Pada Sistem Operasi Proprietary,” J. Ilm. Ilk., vol. 8, no. 3, pp. 242–247, 2016.

A. W. Muhammad, I. Riadi, and Sunardi, “Analisis Statistik Log Jaringan Untuk Deteksi Serangan Ddos Berbasis Neural Network,” J. Ilm. Ilk., vol. 8, no. Desember, pp. 220–225, 2016.

I. N. T. Wirawan and I. Eksistyanto, “Penerapan Naive Bayes Pada Intrusion Detection System Dengan Diskritisasi Variabel,” J. Ilm. Teknol. Inf., vol. 13, pp. 182–189, 2015.

N. T. Thomopoulos, Statistical Distributions. 2017.

J. Gondohanindijo, “Sistem Untuk Mendeteksi Adanya Penyusup ( IDS : Intrusion Detection System ),” Semarang, vol. 2, pp. 46–54, 2011.

Y. S. Nugroho, “Data Mining Menggunakan Algoritma Naïve Bayes untuk Klasifikasi Kelulusan Mahasiswa Universitas Dian Nuswantoro,” J. Semant. 2013, pp. 1–11, 2009.

C. Yang, G. N. Odvody, C. J. Fernandez, J. A. Landivar, R. R. Minzenmayer, and R. L. Nichols, “Evaluating unsupervised and supervised image classification methods for mapping cotton root rot,” Precis. Agric., vol. 16, no. 2, pp. 201–215, 2015.

S. P. H. Pb, “Scatterplots and Correlation,” Growth (Lakeland), 2003.




DOI: http://dx.doi.org/10.33096/ilkom.v11i1.361.17-24

Article Metrics

This article has been viewed : 26 times
PDF files viewed : 18 times

Refbacks

  • There are currently no refbacks.


View Visitor
 
Free counters!

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