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Erschienen in:
Ford Motor Side-View Recognition System Based on Wavelet Entropy and Back Propagation Neural Network and Levenberg-Marquardt Algorithm Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Intrusion Detection Based on Self-adaptive Differential Evolution Extreme Learning Machine with Gaussian Kernel

verfasst von : Junhua Ku, Bing Zheng

Erschienen in: Parallel Architecture, Algorithm and Programming

Verlag: Springer Singapore

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Abstract

In our everyday life, intrusion detection system(IDS) becomes a promising area of research in the domain of security. With the rapid development of network-based services, IDS can detect the intruders who are not authorized to the present computer system, so IDS has emerged as an essential component and an important technique for network security.
In order to conquer the disadvantage of the traditional algorithm for single-hidden layer feedforward neural network (SLFN), an improved algorithm, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. ELM is an interested area of research for detecting possible intrusions and attacks. In this paper, we propose an improved learning algorithm named self-adaptive differential evolution extreme learning machine with Gaussian Kernel (SaDE-KELM) for classifying and detecting the intrusions. We compare our methods with commonly used ELM, DE-ELM techniques in classifications. Simulation results show that the proposed SaDE-KELM approach achieves higher detection accuracy in classification case.

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Vorheriges Kapitel Ford Motor Side-View Recognition System Based on Wavelet Entropy and Back Propagation Neural Network and Levenberg-Marquardt Algorithm
Nächstes Kapitel Prediction for Passenger Flow at the Airport Based on Different Models
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Metadaten
Titel
Intrusion Detection Based on Self-adaptive Differential Evolution Extreme Learning Machine with Gaussian Kernel
verfasst von
Junhua Ku
Bing Zheng
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Parallel Architecture, Algorithm and Programming

Print ISBN: 978-981-10-6441-8

Electronic ISBN: 978-981-10-6442-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-6442-5_2

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