Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines

Setareh Roshan, Yoan Miche*, Anton Akusok, Amaury Lendasse

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

26 Citations (Scopus)

Abstract

Despite the large volume of research conducted in the field of intrusion detection, finding a perfect solution of intrusion detection systems for critical applications is still a major challenge. This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems. The main objective of this paper is to propose an adaptive design of intrusion detection systems on the basis of Extreme Learning Machines. The proposed system offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in a cost-effective manner.

Original languageEnglish
Peer-reviewed scientific journalJournal of the Franklin Institute : Engineering and Applied Mathematics
Volume355
Issue number4
Pages (from-to)1752-1779
Number of pages28
ISSN0016-0032
DOIs
Publication statusPublished - 13.07.2018
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management

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