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

被引：50

作者：

Roshan, Setareh ^{[1
,2
]}

Miche, Yoan ^{[3
]}

Akusok, Anton ^{[4
]}

Lendasse, Amaury ^{[5
,6
]}

机构：

[1] F Secure Corp, Tammasaarenkatu 7, Helsinki 00180, Finland

[2] Aalto Univ, Sch Sci, Dept Comp Sci, Konemiehentie 2, Espoo 02150, Finland

[3] Nokia Bell Labs, Karakaari 13, Espoo 02760, Finland

[4] Arcada Univ Appl Sci, Jan Magnus Janssonin Aukio 1, Helsinki 00560, Finland

[5] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA USA

[6] Univ Iowa, Iowa Informat Initiat, Iowa City, IA USA

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2018年 / 355卷 / 04期

关键词：

ELM;

D O I：

10.1016/j.jfranklin.2017.06.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems.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. (c) 2017 The Frank linInstitute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1752 / 1779

页数：28

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