A novel hybrid KPCA and SVM with GA model for intrusion detection

Kuang, Fangjun; Wang, Xu; Zhang, Siyang

doi:10.1016/j.asoc.2014.01.028

Cited by 390 publications

(182 citation statements)

References 12 publications

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“…The first stage classification performs an unsupervised classification though clustering the entire dataset and followed by Multiclass Support vector machine classifier at the second phase. Chi-SVM [13] SVM-GA [11] Proposed…”

Section: Resultsmentioning

confidence: 99%

Enhancing the Performance of an Intrusion Detection System Through Multi-Linear Dimensionality Reduction and Multi-Class SVM

Kumar¹,

Raju²,

Vardhan³

2018

IJIES

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Abstract:With the huge development of the usage of computer over network and advancement in applications running on various platforms captures the attention towards network security. The Intrusion Detection System (IDS) plays a vital role in detecting anomalies and attacks in the network. Earlier approaches of IDS relied on Machine Learning (M L) techniques. Due to some limitations, a better approach is needed. A combination of Machine Learning techniques and data preprocessing is an effective approach for IDS. In this work, a new dimensionality reduction technique combined with the Multi-class SVM (Support Vector Machine) is proposed for intrusion detection. In the proposed model, Multi-Linear Dimensionality Reduction (ML-DR) is proposed as a feature extraction technique to reduce the dimension in order to shorten the training time. A Multi-class SVM (M-SVM) is used to detect whether the action is an attack or not. Here the Multi-class SVM is adopted to perform multi-attack classification in a layered fashion. Radial Basis Function Kernel is used as a SVM kernel. NSL-KDD data set is used for the performance evaluation of the proposed approach. The performance metrics such as classification accuracy, false alarm rate and the correlation coefficient are evaluated to measure its efficiency. In comparison with other detection approaches, the experimental results show that the proposed model outperforms the higher classification accuracy.

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Section: Resultsmentioning

confidence: 99%

Enhancing the Performance of an Intrusion Detection System Through Multi-Linear Dimensionality Reduction and Multi-Class SVM

Kumar¹,

Raju²,

Vardhan³

2018

IJIES

View full text Add to dashboard Cite

show abstract

“…The scatter matrix for zero mean data is given by C = FF T . Then, a kernel matrix can be constructed as K = F T F. Using the kernel trick, the centered kernel matrix can be expressed as follows 14,15 …”

Section: Feature Extractionmentioning

confidence: 99%

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Sheng

Dong

et al. 2016

Advances in Mechanical Engineering

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To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time-frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

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“…[21][22][23][24][25] The covariance matrix C F should be constructed for principal component (PC) analysis in the high dimension feature space F. Through the data processing that above z-score normalization method makes P nk = 1 F(x k ) = 0, and the covariance matrix is afford by…”

Section: Kpcamentioning

confidence: 99%

A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

Feng

Xiao

Liu

et al. 2016

Advances in Mechanical Engineering

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Remaining useful life estimation of the prognostics and health management technique is a complicated and difficult research question for maintenance. In this article, we consider the problem of prognostics modeling and estimation of the turbofan engine under complicated circumstances and propose a kernel principal component analysis-based degradation model and remaining useful life estimation method for such aircraft engine. We first analyze the output data created by the turbofan engine thermodynamic simulation that is based on the kernel principal component analysis method and then distinguish the qualitative and quantitative relationships between the key factors. Next, we build a degradation model for the engine fault based on the following assumptions: the engine has only had constant failure (i.e. no sudden failure is included), and the engine has a Wiener process, which is a covariate stand for the engine system drift. To predict the remaining useful life of the turbofan engine, we built a health index based on the degradation model and used the method of maximum likelihood and the data from the thermodynamic simulation model to estimate the parameters of this degradation model. Through the data analysis, we obtained a trend model of the regression curve line that fits with the actual statistical data. Based on the predicted health index model and the data trend model, we estimate the remaining useful life of the aircraft engine as the index reaches zero. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this prediction method that we propose. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this proposed method, the precision of the method can reach to 98.9% and the average precision is 95.8%.

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A novel hybrid KPCA and SVM with GA model for intrusion detection

Cited by 390 publications

References 12 publications

Enhancing the Performance of an Intrusion Detection System Through Multi-Linear Dimensionality Reduction and Multi-Class SVM

Enhancing the Performance of an Intrusion Detection System Through Multi-Linear Dimensionality Reduction and Multi-Class SVM

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

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