Intrusion detection in high-speed big data networks: A comprehensive approach

Kamran Siddique, Zahid Akhtar, Yangwoo Kim

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

Abstract

In network intrusion detection research, two characteristics are generally considered vital to build efficient intrusion detection systems (IDSs) namely, optimal feature selection technique and robust classification schemes. However, an emergence of sophisticated network attacks and the advent of big data concepts in anomaly detection domain require the need to address two more significant aspects. They are concerned with employing appropriate big data computing framework and utilizing contemporary dataset to deal with ongoing advancements. Based on this need, we present a comprehensive approach to build an efficient IDS with the aim to strengthen academic anomaly detection research in real-world operational environments. The proposed system is a representative of the following four characteristics: It (i) performs optimal feature selection using branch-and-bound algorithm; (ii) employs logistic regression for classification; (iii) introduces bulk synchronous parallel processing to handle computational requirements of large-scale networks; and (iv) utilizes real-time contemporary dataset named ISCX-UNB to validate its efficacy.

Original languageEnglish
Title of host publicationAdvances in Computer Science and Ubiquitous Computing - CSA-CUTE 17
EditorsGangman Yi, Yunsick Sung, James J. Park, Vincenzo Loia
PublisherSpringer Verlag
Pages1364-1370
Number of pages7
ISBN (Print)9789811076046
DOIs
StatePublished - 2018
EventInternational Conference on Computer Science and its Applications, CSA 2017 - Taichung, Taiwan, Province of China
Duration: 18 Dec 201720 Dec 2017

Publication series

NameLecture Notes in Electrical Engineering
Volume474
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Computer Science and its Applications, CSA 2017
Country/TerritoryTaiwan, Province of China
CityTaichung
Period18/12/1720/12/17

Keywords

  • Anomaly detection
  • Big data
  • BSP
  • Bulk synchronous parallel
  • Darpa
  • ISCX-UNB dataset
  • KDD Cup ’99
  • Network intrusion detection systems

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