Evaluation of the performance of supervised and unsupervised Machine learning techniques for intrusion detection

Fernando Gutierrez Portela, Florina Almenares Mendoza, Liliana Calderon Benavides

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

4 Scopus citations

Abstract

machine learning techniques are widely used in the research for intelligent solutions anomalies detection on different computers and communications systems, which have allowed to modernize the intrusion detection systems, to ensure data privacy. For that, this paper evaluates the performance of some supervised (i.e., KNN and SVM) and unsupervised (i.e., Isolation Forest and K-Means) algorithms, for intrusion detection, using data set UNSW-NB12. The results show that the supervised algorithm SVM gaussiana fine, obtained 92% in accuracy, indicating the ability to correctly classify normal and abnormal data. With regard to the unsupervised algorithms, the K-Means algorithm groups the data together correctly and allows the appropriate number of groups to be clearly defined; however, this data set is highly agglomerated. For Isolation Forest, despite being a robust algorithm for the separation of atypical values, it presented difficulty for it. Finally, it should be made clear that not all methods of detecting anomalies by distance work properly for all data sets.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Applied Science and Advanced Technology, iCASAT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131108
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Applied Science and Advanced Technology, iCASAT 2019 - Queretaro, Mexico
Duration: 27 Nov 201928 Nov 2019

Publication series

Name2019 IEEE International Conference on Applied Science and Advanced Technology, iCASAT 2019

Conference

Conference2019 IEEE International Conference on Applied Science and Advanced Technology, iCASAT 2019
Country/TerritoryMexico
CityQueretaro
Period27/11/1928/11/19

Keywords

  • Clustering algorithms
  • Intrusion detection
  • Machine learning algorithms
  • Outlier detection
  • Supervised algorithms
  • Unsupervised algorithms

Fingerprint

Dive into the research topics of 'Evaluation of the performance of supervised and unsupervised Machine learning techniques for intrusion detection'. Together they form a unique fingerprint.

Cite this