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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2605.02015

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

Institutions: The University of Texas at Dallas, DevCom (Czechia), Virginia Tech, DEVCOM Army Research Laboratory, University of California, Irvine

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7160288381

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