An efficient intrusion detection system based on support vector machines and gradually feature removal method

By Yinhui Li, Jingbo Xia, Silan Zhang, Jiakai Yan, Xiaochuan Ai, Kuobin Dai. Expert systems with applications, 2012

397 citations recorded by the source corpus.

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Selecting critical features for data classification based on machine learning methods (2020, background): “The investigation improves understanding of the nature of variable importance in RF. RF has been discussed as a robust learner in several domains [18, 19].”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “In the work by Li et al., an SVM classifier with an RBF kernel was applied to classify the KDD 1999 dataset into predefined classes (Li et al., 2012).”
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection (2018, background): “Over the last decade many machine learning techniques have been proposed to improve detection performance [2], [3], [4].”
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]”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “[96], an SVM classifier with an RBF kernel was used to classify the KDD 1999 data set into predefined categories (DoS, Probe or Scan, U2R, R2L, and Normal).”