Mutual information-based feature selection for intrusion detection systems

By Fatemeh Amiri, M. Yousefi, C. Lucas, Azadeh Shakery, N. Yazdani. Journal of Network and Computer Applications, 2011

467 citations recorded by the source corpus.

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A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “65%, but when MMIFS [133] feature selection technique is applied which uses only 8 features, its detection rate is improved to 86.”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, methodology): “Amiri et al. Amiri et al. (2011) developed a simple and effective feature selection technique according to mutual information technique.”
Feature selection in machine learning: A new perspective (2018, background): “such as image recognition [10-14], image retrieval [15-17], text mining [18-20], intrusion detection [21-23], bioinformatic data analysis [24-31], fault diagnosis [32-34], and so on.”
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “As an enhancement of Mutual Information Feature Selection (MIFS) [11] and Modified Mutual Informationbased Feature Selection (MMIFS) [12], the proposed feature selection method does not have any free parameter, such as b in MIFS and MMIFS.”
Network Anomaly Detection: Methods, Systems and Tools (2014, methodology): “Several other methods for feature selection are [39], [63]–[65].”