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

By Fangjun Kuang, Weihong Xu, Siyang Zhang. Applied Soft Computing, 2014

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Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection (2018, methodology): “[2] applied a hybrid model of SVM andKPCA with GA to intrusion detection, and their system showed 96% detection rate.”
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset (2020, methodology): “Machine learning methods [3]–[8] have been widely used in intrusion detection to identify malicious traffic.”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “[137] [138] [156] [140] [28] [141] [142] [143] [144] [159] [160]”
An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks (2019, methodology): “[9] F. Kuang,W. Xu, and S. Zhang, ‘‘A novel hybrid KPCA and SVMwith GA model for intrusion detection,’’ Appl.”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, methodology): “Kuang et al. (2014) proposed a novel support vector machine combining kernel principal component analysis (KPCA) with genetic algorithm.”
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks (2017, methodology): “[10] F. Kuang,W. Xu, and S. Zhang, ‘‘A novel hybrid KPCA and SVMwith GA model for intrusion detection,’’ Appl.”