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

cs.LGMay 3, 2026

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

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

Organizations: University of Texas at Dallas, Richardson, TX · DEVCOM, Army Research Laboratory, Adelphi, MD · Department of Computer Science, Virginia Tech, Blacksburg, VA · University of California, Irvine

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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Jun 10, 2026 · cs.CRJ/K move · Enter open · S save

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

Mayank Raj, Nathaniel D. Bastian, Lance Fiondella, Gokhan Kul

Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. While prior work has demonstrated adversarial vulnerabilities in isolated settings, systematic cross-architecture as well as class and category of attack based comparisons under controlled attack conditions remain limited, leaving practitioners without clear guidance on which models to deploy in adversarial environments. This paper asks a simple question: what type of classifier architectures actually hold up when attackers try to manipulate the systems? We put three popular architectures through their paces: a 1D Convolutional Neural Network, a Long Short-Term Memory (LSTM) network, and a Random Forest (RF) ensemble. Using the ACI-IoT-2023 dataset (over 1.2 million samples spanning 12 attack types), we subject each model with FGSM and PGD adversarial attacks, which apply gradient-based perturbations in normalized feature space consistent with established adversarial ML evaluation protocols, at perturbation budgets ranging from

ε=0.01

ε=0.1

. Surprisingly, Random Forest achieved near-perfect baseline accuracy (99.98%), yet collapsed catastrophically under attack, dropping 73 percentage points at the smallest perturbation we tested. CNN, on the other hand, retained 95.5% accuracy at

ε=0.01

and degraded gracefully as perturbations increased. LSTM fell somewhere in between. These findings flip the conventional wisdom where high baseline accuracy means nothing if a model shatters at the first sign of adversarial pressure. For practitioners deploying intrusion detection in adversarial environments, we recommend CNN-based architectures and provide scenario-specific deployment guidance.

Intrusion Detection Adversarial Examples Adversarial Evaluation Machine Learning Internet Of Thing

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Abstract

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Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

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