An analysis of “A feature reduced intrusion detection system using ANN classifier” by Akashdeep et al. expert systems with applications (2017)
作者：
Highlights：
• Test dataset can never be modified.
• Optimal Training dataset composition for detection of minority classes.
• Feature selection without under-sampling performs better.
• Under-sampling Normal class instances is more fruitful compared to oversampling U2R and R2L category of attacks.
• Naïve Bayes Classifier has good detection rate for U2R and Probe category of attacks and can be utilized for the same in a multistage classifier.
摘要
•Test dataset can never be modified.•Optimal Training dataset composition for detection of minority classes.•Feature selection without under-sampling performs better.•Under-sampling Normal class instances is more fruitful compared to oversampling U2R and R2L category of attacks.•Naïve Bayes Classifier has good detection rate for U2R and Probe category of attacks and can be utilized for the same in a multistage classifier.
论文关键词：Intrusion Detection System (IDS),Feature selection,Training-Test dataset composition
论文评审过程：Received 14 December 2018, Revised 8 April 2019, Accepted 8 April 2019, Available online 9 April 2019, Version of Record 16 April 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.017
