Network Intrusion Detection Using Flow Statistics

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

3 Citations (Scopus)

Abstract

The increasing use of network data within every aspect of human life, ranging from genetic databases to credit card payments, urges for efficient methods for detecting any attempts (intrusions) to compromise sensitive information. The problem of detecting such network intrusions is challenging, since the regular or normal network patterns are permanently changing. This paper discusses a novel intrusion detection system based on using histograms of network parameters as features which are then fed into an extreme learning machine for classifying network flows. We evaluate and compare the proposed method with existing approaches using the ISCX-IDS 2012 benchmark dataset. The numerical experiments indicate that the proposed method outperforms existing approaches by achieving an average detection rate of up to 99% while suffering a misclassification rate of only 2 %.

Original languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PublisherIEEE
Pages70-74
Number of pages5
ISBN (Print)9781538615706
DOIs
Publication statusPublished - 29 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Statistical Signal Processing Workshop - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018
Conference number: 20

Workshop

WorkshopIEEE Statistical Signal Processing Workshop
Abbreviated titleSSP
CountryGermany
CityFreiburg im Breisgau
Period10/06/201813/06/2018

Keywords

  • distribution estimation
  • extreme learning machine
  • feature learning
  • hierarchical clustering
  • Intrusion detection

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