Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection

Authors

  • Saipul Anwar Universitas Tanri Abeng
  • Fajar Septian Universitas Pamulang
  • Ristasari Dwi Septiana STMIK ERESHA

Keywords:

correlation-based fetaure selection, classification, data mining, intrusion detection system, naïve bayes

Abstract

Intrusion Detection System (IDS) is useful for detecting an attack or disturbance on a network or information system. Anomaly detection is a type of IDS that can detect a deviate attack on the network based on statistical probability. The increasing use of the internet also increases interference or attacks from intruders or crackers that exploit weak internet protocols and application software. When many data packets arrive, a problem arises that needs to be analyzed. The right technique to analyze the data package is data mining. This study aims to classify IDS anomalies using the Naïve Bayes classification algorithm from the results of attribute selection with correlation-based feature selection. This study uses a UNSW-NB15 intrusion detection system data collection consisting of 49 attributes and 321,283 data records. Performance measurements are based on accuracy, precision, F-Measure and ROC Area. The results of attribute selection with correlation-based feature selection leave 4 attributes. The results of the evaluation of IDS anomaly classification using the naïve Bayes algorithm without the precedence of the attributes selected by the correlation technique obtained an accuracy rate of 71.2%. While the classification results if preceded by the attributes selected by the correlation technique obtained an accuracy of 74.8%. Classification with the naïve Bayes algorithm can be improved its accuracy which is preceded by the selection of attributes with correlation techniques.

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Published

2019-10-30

How to Cite

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. Retrieved from https://openjournal.unpam.ac.id/index.php/JTSI/article/view/3453

Issue

Vol. 2 No. 4 (2019): Jurnal Teknologi Sistem Informasi dan Aplikasi

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Article

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Copyright (c) 2019 Saipul Anwar, Fajar Septian, Ristasari Dwi Septiana

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