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

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

*Motsvarande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

50 Citeringar (Scopus)

Sammanfattning

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 This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems.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.

OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftJournal of the Franklin Institute : Engineering and Applied Mathematics
Volym355
Utgåva4
Sidor (från-till)1752-1779
Antal sidor28
ISSN0016-0032
DOI
StatusPublicerad - 13.07.2018
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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