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All Journal ILKOM Jurnal Ilmiah
Aini, Fadhilah Dhinur
Univeristas Muslim Indonesia
Computer Science & IT

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ANALISIS PERBANDINGAN DETECTION TRAFFIC ANOMALY DENGAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) Riadi, Imam; Umar, Rusydi; Aini, Fadhilah Dhinur
ILKOM Jurnal Ilmiah Vol 11, No 1 (2019)
Publisher : Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (770.776 KB) | DOI: 10.33096/ilkom.v11i1.361.17-24

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.
Co-Authors Imam Riadi Rusydi Umar