Principal Component Neural Networks Based Intrusion Feature Extraction and Detection Using SVM

Gao, Haihua; Yang, Huihua; Wang, Xingyu

doi:10.1007/11539117_4

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…In addition, the feature selection can be formulated as a multi‐objective optimization problem, so a wide variety of optimization algorithms such as evolutionary ones can be used for this purpose . In this way, several studies have used different statistical and data mining‐based feature selection methods such as Bayesian network , classification and regression tree , mutual information , flexible neural tree , principal component neural networks , and hybrid of rough set theory and particle swarm optimization (PSO) algorithm .…”

Section: Introductionmentioning

confidence: 99%

Using particle swarm optimization in fuzzy association rules‐based feature selection and fuzzy ARTMAP‐based attack recognition

Sheikhan

Rad

2012

Security Comm Networks

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Feature selection is a classic research topic in data mining, and it has attracted much interest in many fields such as network security. In addition, data mining approaches such as fuzzy association rule mining (FARM) can improve the performance of intrusion detection systems. In this study, a FARM‐based feature selector is proposed in order to reduce the dimension of input features to the misuse detector. Furthermore, a fuzzy ARTMAP neural network is used as the classifier. The accuracy of the proposed approach depends strongly on the precision of the parameters of FARM‐based feature selector module and fuzzy ARTMAP neural classifier. Particle swarm optimization (PSO) algorithm is incorporated into the proposed method to determine optimum values of parameters. In this way, the performance of PSO algorithm is compared with genetic algorithm (GA), as well. Experimental results indicate that PSO outperforms GA both in population size and number of evolutions and can converge faster. This is very important for enhancing the mining performance in large datasets such as intrusion detection datasets. When compared with some other machine learning methods, the proposed system indicates better performance in terms of detection rate, false alarm rate, and cost per example. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Using particle swarm optimization in fuzzy association rules‐based feature selection and fuzzy ARTMAP‐based attack recognition

Sheikhan

Rad

2012

Security Comm Networks

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“…He used autoregressive moving average (ARMA) and Hopfield models to analyze the time series. Gao et al (Gao et al, 2005) proposed a method of applying principal component neural networks for intrusion feature extraction. The extracted features are employed by SVM for classification.…”

Section: One Of the Most Popular Rule Induction Techniques Used In Idmentioning

confidence: 99%

Abstraction, aggregation and recursion for generating accurate and simple classifiers

Honavar

Kang

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Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining

Sheikhan

Rad

2012

Neural Comput & Applic

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Principal Component Neural Networks Based Intrusion Feature Extraction and Detection Using SVM

Cited by 4 publications

References 7 publications

Using particle swarm optimization in fuzzy association rules‐based feature selection and fuzzy ARTMAP‐based attack recognition

Using particle swarm optimization in fuzzy association rules‐based feature selection and fuzzy ARTMAP‐based attack recognition

Abstraction, aggregation and recursion for generating accurate and simple classifiers

Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining

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