Adversarially robust few-shot learning for aI-enabled attack mitigation in the internet of things

Abstract

The rapid expansion of Internet of Things (IoT) environments has introduced significant security challenges, particularly due to limited labeled data and increasing vulnerability to adversarial attacks. Traditional intrusion detection systems (IDS) struggle to generalize under such constraints, while existing few-shot learning (FSL) approaches remain sensitive to adversarial perturbations. This work proposes a hybrid intrusion detection framework that integrates prototypical few-shot learning with adversarial training to address both data scarcity and robustness. The proposed model employs a Prototypical Network to learn discriminative embeddings from limited samples and incorporates adversarial optimization into the episodic training process to enhance resilience against gradient-based attacks, including FGSM, C&W, DeepFool, and PGD. Experimental evaluation on the IDSIoT2024 dataset demonstrates that the proposed framework achieves high classification performance with only three samples per class, attaining over \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93\%$$\end{document} accuracy under benign conditions. In adversarial settings, performance initially degrades significantly but is restored to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$80-90\%$$\end{document} accuracy after adversarial training under white-box gradient-based attacks. The results confirm that integrating adversarial training within the few-shot learning paradigm improves both generalization and robustness, making the proposed approach suitable for secure and resource-constrained IoT environments.