LDFGB Algorithm for Anomaly Intrusion Detection - Information and Communication Technology Access content directly
Conference Papers Year : 2014

LDFGB Algorithm for Anomaly Intrusion Detection

Zhi-Guo Chen
  • Function : Author
  • PersonId : 993462
Sung-Ryul Kim
  • Function : Author
  • PersonId : 993463

Abstract

With the development of internet technology, more and more risks are appearing on the internet and the internet security has become an important issue. Intrusion detection technology is an important part of internet security. In intrusion detection, it is important to have a fast and effective method to find out known and unknown attacks. In this paper, we present a graph-based intrusion detection algorithm by outlier detection method which is based on local deviation factor (LDFGB). This algorithm has better detection rates than a previous clustering algorithm. Moreover, it is able to detect any shape of cluster and still keep high detection rate for detecting unknown or known attacks. LDFGB algorithm uses graph-based cluster algorithm (GB) to get an initial partition of dataset which depends on a parameter of cluster precision, then we use the outlier detection algorithm to further processing the results of graph-based cluster algorithm. This measure is effective to improve the detection rates and false positive rates.
Fichier principal
Vignette du fichier
978-3-642-55032-4_39_Chapter.pdf (132.54 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01397240 , version 1 (15-11-2016)

Licence

Attribution

Identifiers

Cite

Shang-Nan Yin, Zhi-Guo Chen, Sung-Ryul Kim. LDFGB Algorithm for Anomaly Intrusion Detection. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.396-404, ⟨10.1007/978-3-642-55032-4_39⟩. ⟨hal-01397240⟩
160 View
105 Download

Altmetric

Share

Gmail Facebook X LinkedIn More