Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.06099 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929712207495168 |
|---|---|
| author | Vasudevan, Shrihari Chokshi, Ishan Ranganathan, Raaghul Sundaram, Nachiappan |
| author_facet | Vasudevan, Shrihari Chokshi, Ishan Ranganathan, Raaghul Sundaram, Nachiappan |
| contents | Network Intrusion Detection Systems (IDS) have become increasingly important as networks become more vulnerable to new and sophisticated attacks. Machine Learning (ML)-based IDS are increasingly seen as the most effective approach to handle this issue. However, IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models. Different from existing data-driven techniques to handling class imbalance, this paper explores a structural approach to handling class imbalance in multi-class classification (MCC) problems. The proposed approach - Sequential Binary Classification (SBC), is a hierarchical cascade of (regular) binary classifiers. Experiments on benchmark IDS datasets demonstrate that the structural approach to handling class-imbalance, as exemplified by SBC, is a viable approach to handling the issue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sequential Binary Classification for Intrusion Detection Vasudevan, Shrihari Chokshi, Ishan Ranganathan, Raaghul Sundaram, Nachiappan Cryptography and Security Machine Learning Networking and Internet Architecture Network Intrusion Detection Systems (IDS) have become increasingly important as networks become more vulnerable to new and sophisticated attacks. Machine Learning (ML)-based IDS are increasingly seen as the most effective approach to handle this issue. However, IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models. Different from existing data-driven techniques to handling class imbalance, this paper explores a structural approach to handling class imbalance in multi-class classification (MCC) problems. The proposed approach - Sequential Binary Classification (SBC), is a hierarchical cascade of (regular) binary classifiers. Experiments on benchmark IDS datasets demonstrate that the structural approach to handling class-imbalance, as exemplified by SBC, is a viable approach to handling the issue. |
| title | Sequential Binary Classification for Intrusion Detection |
| topic | Cryptography and Security Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2406.06099 |