Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it woul...

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Bibliographic Details
Published in:Energies (Basel) 2019-03, Vol.12 (7), p.1223
Main Authors: Gao, Jianlei, Chai, Senchun, Zhang, Baihai, Xia, Yuanqing
Format: Article
Language:English
Subjects:
adaptive-principal component analysis (A-PCA)
Algorithms
Anomalies
Artificial intelligence
Collision avoidance
Covariance matrix
Data mining
Datasets
Design
Embedded systems
Fault diagnosis
incremented extreme learning machine (I-ELM)
Internet
Internet of Things
Intrusion detection systems
Laboratories
Learning algorithms
Machine learning
Methods
network intrusion detection (IDS)
Neural networks
NSL-KDD
Principal components analysis
R&D
Research & development
Smart cities
Surveillance
Taxonomy
UNSW-NB15
Wireless networks
Working conditions
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Summary:Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12071223