TY - JOUR
T1 - Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines
AU - Roshan Kokabha, Setareh
AU - Miche, Yoan
AU - Akusok, Anton
AU - Lendasse, Amaury
PY - 2018
Y1 - 2018
N2 - 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 This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems.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.
AB - 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 This is mainly due to the continuous emergence of security threats which can bypass the outdated intrusion detection systems.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.
UR - http://www.scopus.com/inward/record.url?scp=85025432266&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2017.06.006
DO - 10.1016/j.jfranklin.2017.06.006
M3 - Article
AN - SCOPUS:85025432266
SN - 0016-0032
VL - 355
SP - 1752
EP - 1779
JO - JOURNAL OF THE FRANKLIN INSTITUTE: ENGINEERING AND APPLIED MATHEMATICS
JF - JOURNAL OF THE FRANKLIN INSTITUTE: ENGINEERING AND APPLIED MATHEMATICS
IS - 4
ER -