Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhou, Yan Hamlen, Kevin De Lucia, Michael Kantarcioglu, Murat Khan, Latifur Mehrotra, Sharad Swami, Ananthram Thuraisingham, Bhavani |
| author_facet | Zhou, Yan Hamlen, Kevin De Lucia, Michael Kantarcioglu, Murat Khan, Latifur Mehrotra, Sharad Swami, Ananthram Thuraisingham, Bhavani |
| contents | Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traffic, yet deploying such models remains impractical on resource-constrained devices with limited processing power and memory. In this paper, we present a correlation-aware divide-and-conquer learning technique that decomposes a complex learning problem into smaller, more manageable subproblems. This enables lightweight models as simple as decision trees to be trained on focused subtasks, yielding up to 43.3% higher local accuracy and up to 257 times reduction in model size on real-world network intrusion detection datasets, while also improving adversarial robustness and explainability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection Zhou, Yan Hamlen, Kevin De Lucia, Michael Kantarcioglu, Murat Khan, Latifur Mehrotra, Sharad Swami, Ananthram Thuraisingham, Bhavani Machine Learning Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traffic, yet deploying such models remains impractical on resource-constrained devices with limited processing power and memory. In this paper, we present a correlation-aware divide-and-conquer learning technique that decomposes a complex learning problem into smaller, more manageable subproblems. This enables lightweight models as simple as decision trees to be trained on focused subtasks, yielding up to 43.3% higher local accuracy and up to 257 times reduction in model size on real-world network intrusion detection datasets, while also improving adversarial robustness and explainability. |
| title | Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.02015 |