Intrusion Detection Using Unsupervised Approach

Jai Singh; Nizar Bouguila

Emerging Technologies for Developing Countries. First International EAI Conference, AFRICATEK 2017, Marrakech, Morocco, March 27-28, 2017 Proceedings

Research Article

Intrusion Detection Using Unsupervised Approach

Download

269 downloads

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-319-67837-5_18,
    author={Jai Singh and Nizar Bouguila},
    title={Intrusion Detection Using Unsupervised Approach},
    proceedings={Emerging Technologies for Developing Countries. First International EAI Conference, AFRICATEK 2017, Marrakech, Morocco, March 27-28, 2017 Proceedings},
    proceedings_a={AFRICATEK},
    year={2017},
    month={10},
    keywords={Mixture models Intrusion Aitchison distance Feature selection},
    doi={10.1007/978-3-319-67837-5_18}
}

Jai Singh
Nizar Bouguila
Year: 2017
Intrusion Detection Using Unsupervised Approach
AFRICATEK
Springer
DOI: 10.1007/978-3-319-67837-5_18

Jai Singh¹^,*, Nizar Bouguila¹^,*

1: Concordia University

*Contact email: jaipuneet.singh@mail.concordia.ca, nizar.bouguila@concordia.ca

Abstract

The process of detecting intrusion on network traffic has always remained a key concern for security researchers. During the previous years, intrusion detection had attracted many researchers to find anomaly on NSL-KDD data set. Hence, most of the approaches applied on NSL KDD data set were supervised approaches. We had conducted statistical analysis on this data set using Dirichlet Mixture model. We have seen initialization using Aitchison distance fits better for proportional data. The feature selection highly affects both the performance and results into an improved evaluation of anomaly detection by an unsupervised approach.

Keywords: Mixture models Intrusion Aitchison distance Feature selection

Published: 2017-10-25
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-67837-5_18