@inproceedings{173f77d72f034a6a9920bf6b18c8bd1a,
title = "Improving Network Intrusion Detection Using Supervised Learning for Feature Selection",
abstract = "Detecting network intrusions is a crucial area of study in computer security research, with the goal of identifying malicious activities in computer networks. Prior research has concentrated on utilizing machine learning methods to spot network intrusions, typically by training models on distinct attack categories. In our study, we suggest a revised method that merges three attack categories into a unified category through supervised learning. This approach seeks to streamline the classification procedure and enhance the precision of network intrusion detection.",
keywords = "Accuracy, Attacks, Classification, Intrusion Detection, Machine Learning, Supervised Learning",
author = "Hamed Sanusi and Zheni Utic and Jongyeop Kim",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023 ; Conference date: 14-12-2023 Through 16-12-2023",
year = "2023",
doi = "10.1109/BCD57833.2023.10466354",
language = "English",
series = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "42--48",
editor = "Jongwoo Park and Lan, {Ngo Thi Phuong} and Sungtaek Lee and Tien, {Tran Anh} and Jongbae Kim",
booktitle = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",
address = "United States",
}