Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection
Abstract
One of the key challenges of machine learning (ML) based intrusion detection system (IDS) is the expensive computational complexity which is largely due to redundant, incomplete, and irrelevant features contain in the IDS datasets. To overcome such challenge and ensure building an efficient and more accurate IDS models, many researchers utilize preprocessing techniques such as normalization and feature selection in a hybrid modeling approach. In this work, we propose a hybrid IDS modeling approach with an algorithm for feature selection (FS) and another for building an IDS. The FS algorithm is a wrapper-based with a decision tree as the feature evaluator. The propose FS method is used in combination with some selected ML algorithms to build IDS models using the UNSW-NB15 dataset. Some IDS models are built as a baseline in a single modeling approach using the full features of the dataset. We evaluate the effectiveness of our propose method by comparing it with the baseline models and also with state-of-the-art works. Our method achieves the best DR of 97.95% and shown to be quite effective in comparison to state-of-the-art works. We, therefore, recommend its usage especially in IDS modeling with the UNSW-NB15 dataset.
- Publication:
-
arXiv e-prints
- Pub Date:
- August 2020
- DOI:
- 10.48550/arXiv.2008.07405
- arXiv:
- arXiv:2008.07405
- Bibcode:
- 2020arXiv200807405A
- Keywords:
-
- Computer Science - Cryptography and Security;
- Computer Science - Machine Learning
- E-Print:
- 8 pages, 3 figures, Presented at ICICSE 2020 Conference Proceedings, which will be published in the International Conference Proceedings Series by ACM, and will be archived in the ACM Digital Library