A GA-LR wrapper approach for feature selection in network intrusion detection

By Chaouki Khammassi, Saoussen Krichen. Computers & security, 2017

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9b5c09c49d65f7a0a088408432fefa5cb9262858

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. K. Rhee
2. Marco Comuzzi
3. Bayu Adhi Tama
4. Yanxia Sun
5. Sydney Mambwe Kasongo

In Computer Networks and Communications

1. K. Rhee
2. Marco Comuzzi
3. Bayu Adhi Tama
4. Yanxia Sun
5. Sydney Mambwe Kasongo

In Signal Processing

1. K. Rhee
2. Marco Comuzzi
3. Bayu Adhi Tama
4. Yanxia Sun
5. Sydney Mambwe Kasongo

Citations in the corpus

• Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset (2020, methodology): “Moreover, in comparison to the work presented in [17] whereby the GA-LR-DT was used; the XGBoost-DT attained a test accuracy score of 90.”
• Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset (2021, background): “UNSW-NB15 is a recently proposed dataset for training and testing the performance of AIDS models [27], [28], [29].”
• TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, methodology): “, [17], [20], [51], which have used the same datasets are considered.”