Applying Gini Importance and RFE Methods for Feature Selection in Shallow Learning Models for Implementing Effective Intrusion Detection System

DOI

10.2991/978-94-6463-136-4_21How to use a DOI?

Keywords

Machine Learning; Cyber Security; Intrusion Detection; Shallow Learning; NSL-KDD; CICIDS-2017

Abstract

Cyber security is becoming important concern in the recent world. Number of internet users are increasing day by day and they are accessing huge amount of data on their device from different websites. Attackers are trying to get access to normal user’s systems by introducing different types of attacks. Number of Intrusion Detection Systems are being developed to protect the normal users from the attackers. Most of these systems are developed using outdated datasets, and they are having scope to improve their accuracy, detection rate and to reduce false alarm rate. In the proposed system we are going to train the different machine learning models for binary and multiclass classification. We are using shallow learning algorithms like Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, XGBoost and Ensemble Technique on benchmark NSL-KDD and recent CICIDS-2017 IDS dataset. We have used Recursive Feature Elimination and Feature Importance based features selection on NSL-KDD and Feature Importance based feature selection on CICIDS-2017 dataset. All machine learning models are trained using all features and selected features datasets. Testing is performed on separate test dataset like KDDTest+ as well as test sets obtained by applying train test split on original datasets. It is observed that the performance on feature importance-based feature selection models and 10-fold cross validation models is improved in terms of accuracy, precision, recall and f-measure.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Nilesh G. Pardeshi
AU  - Dipak V. Patil
PY  - 2023
DA  - 2023/05/01
TI  - Applying Gini Importance and RFE Methods for Feature Selection in Shallow Learning Models for Implementing Effective Intrusion Detection System
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 214
EP  - 234
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_21
DO  - 10.2991/978-94-6463-136-4_21
ID  - Pardeshi2023
ER  -