Improving intrusion detection system detection accuracy and reducing learning time by combining selected features selection and parameters optimization

Bisyron Wahyudi Masduki, Kalamullah Ramli

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

3 Citations (Scopus)

Abstract

IDS capability in detecting an attacks is highly dependent on the accuracy of attack detection which usually is represented by the least number of false alarms. In this work we simplify the large network dataset by selecting only the most important and influential features in the dataset to increase the IDS performance and accuracy. The creation of smaller dataset is aimed to decrease time for training the SVM machine learning in detecting attacks. This work designed and built a prototype of IDS equipped with machine learning models to improve accuracy in detecting DoS and R2L attacks. Machine-learning algorithms is added to recognize specific characteristics of the attack at the national Internet network. New methods and techniques developed by combining feature selection and parameter optimization algorithm are then implemented in the Internet monitoring system. Through experiment and analysis, we find out that for DOS attacks the proposed approach improved accuracy for the detection and increased in speed on training and testing phase. Even though limited and appropriate selection of parameters slightly decrease the accuracy in the detection of R2L attacks but our approach significantly increases the speed of the training and testing process.

Original languageEnglish
Title of host publicationProceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages397-402
Number of pages6
ISBN (Electronic)9781509011780
DOIs
Publication statusPublished - 5 Apr 2017
Event6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016 - Batu Ferringhi, Penang, Malaysia
Duration: 25 Nov 201627 Nov 2016

Publication series

NameProceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016

Conference

Conference6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
Country/TerritoryMalaysia
CityBatu Ferringhi, Penang
Period25/11/1627/11/16

Keywords

  • attack
  • intrusion detection system
  • machine-learning
  • support vector machine
  • threat

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