Data Mining Implementation for Monitoring Network Intrusion

Annisa Andarrachmi, Wahyu Catur Wibowo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Information and Communication Network Center (BJIK) is one of the centers in the Agency for the Assessment and Application of Technology (BPPT). BJIK develops a network monitoring information system called Simontik to protect the BPPT system from threats where antivirus softwares and firewalls fail to give the level of protection needed. The random nature of threats makes it difficult to develop a rule-based system to predict the existence of intrusion. In this research, we apply a deep learning model to predict network intrusion. We found that our deep learning model using deep neural network and random forest algorithm can produce 99.91% accuracy compared to 98.11% using support vector machine algorithm.

Original languageEnglish
Title of host publicationICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences
Subtitle of host publicationAccelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728146102
DOIs
Publication statusPublished - Oct 2019
Event3rd International Conference on Informatics and Computational Sciences, ICICOS 2019 - Semarang, Indonesia
Duration: 29 Oct 201930 Oct 2019

Publication series

NameICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings

Conference

Conference3rd International Conference on Informatics and Computational Sciences, ICICOS 2019
CountryIndonesia
CitySemarang
Period29/10/1930/10/19

Keywords

  • data mining
  • deep learning
  • KDD
  • random forest
  • support vector machine

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