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

ArXi:2605.02015v1 Announce Type: new 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, manageable subproblems.