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A new network intrusion detection algorithm: DA‐ROS‐ELM
Author(s) -
Yu Yi,
Kang SongLin,
Qiu He
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22606
Subject(s) - tikhonov regularization , computer science , extreme learning machine , intrusion detection system , algorithm , regularization (linguistics) , generalization , artificial intelligence , artificial neural network , mathematics , mathematical analysis , inverse problem
In this paper, a novel dual adaptive regularized online sequential extreme learning machine (DA‐ROS‐ELM) is proposed to detect network intrusion. The ridge regression factor based on Tikhonov regularization is introduced to solve the over‐fitting and ill‐posed problems. According to the arrived data in each updating phase and all currently available data, dual adaptive mechanism is designed to respectively select the suitable updating mode of output weight β and regularized parameter C . The performance of our algorithm is assessed by NSL‐KDD dataset, and the results show that the DA‐ROS‐ELM can obtain faster training speed, higher accuracy, lower rate of false positive and false negative, and greater generalization performance than other network intrusion detection algorithms. Besides, the adaptive mechanism makes this algorithm can meet the real‐time requirement of the network intrusion system. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.