Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection

Yan Zhou; Kevin Hamlen; Michael De Lucia; Murat Kantarcioglu; Latifur Khan; Sharad Mehrotra; Ananthram Swami; Bhavani Thuraisingham

arXiv · 2605.02015

Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection

Yan Zhou·Kevin Hamlen·Michael De Lucia·Murat Kantarcioglu·Latifur Khan·Sharad Mehrotra·Ananthram Swami·Bhavani Thuraisingham

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Abstract

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.

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Yan Zhou, Kevin Hamlen, Michael De Lucia, Murat Kantarcioglu, Latifur Khan, Sharad Mehrotra, Ananthram Swami, Bhavani Thuraisingham. 2026-05-03. Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection. https://arxiv.org/abs/2605.02015

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