Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

doi: 10.1109/ICMLA55696.2022.00211

Original language	English
Title of host publication	2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Pages	1326-1333
State	Published - 2022

@inproceedings{98dacd9e248847fb8bef06f8120e29a3,

title = "Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems",

abstract = "doi: 10.1109/ICMLA55696.2022.00211",

author = "Adam Lehavi and Seongtae Kim",

year = "2022",

language = "English",

pages = "1326--1333",

booktitle = "2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)",

}

TY - GEN

T1 - Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems

AU - Lehavi, Adam

AU - Kim, Seongtae

PY - 2022

Y1 - 2022

N2 - doi: 10.1109/ICMLA55696.2022.00211

AB - doi: 10.1109/ICMLA55696.2022.00211

M3 - Conference contribution

SP - 1326

EP - 1333

BT - 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

ER -