Sequential Binary Classification for Intrusion Detection

• URL: http://arxiv.org/abs/2406.06099v2
• Date: Thu, 13 Feb 2025 09:35:44 GMT
• Title: Sequential Binary Classification for Intrusion Detection
• Authors: Shrihari Vasudevan, Ishan Chokshi, Raaghul Ranganathan, Nachiappan Sundaram,
• Abstract summary: IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models. This paper explores a structural approach to handling class imbalance in multi-class classification problems. 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.
• Score: 0.0
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• Abstract: 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.

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