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An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning).

Fatma S Alrayes¹, Syed Umar Amin², Nada Hakami²

¹Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive intrusion detection system (IDS) for Internet of Things (IoT) security using Model-Agnostic Meta-Learning (MAML). The MAML-based IDS effectively detects cyber threats with high accuracy across diverse datasets.

Keywords:

Internet of Things (IoT)IoT security Model-Agnostic Meta-Learning (MAML)cybersecurity intrusion detection system (IDS)meta-learning

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Area of Science:

Cybersecurity
Machine Learning
Internet of Things (IoT) Security

Background:

Traditional Intrusion Detection Systems (IDS) struggle with the dynamic and heterogeneous nature of IoT environments.
Emerging Internet of Things (IoT) devices introduce new cybersecurity vulnerabilities.
A need exists for adaptable IDS with innovative strategies to counter evolving cyber threats.

Purpose of the Study:

To develop an adaptive intrusion detection framework for enhanced Internet of Things (IoT) security.
To improve the generalization capabilities of IDS in diverse and changing network conditions.
To leverage Model-Agnostic Meta-Learning (MAML) and few-shot learning for rapid adaptation to new threats with minimal data.

Main Methods:

Utilized Model-Agnostic Meta-Learning (MAML) to train a base model capable of fast adaptation.
Employed few-shot learning paradigms to enable quick learning on new tasks with limited data.
Applied the proposed framework to benchmark datasets UNSW-NB15 and NSL-KDD99 for validation.

Main Results:

Achieved 99.98% accuracy, 99.5% precision, 99.0% recall, and 99.4% F1 score on the UNSW-NB15 dataset.
Attained 99.1% accuracy, 97.3% precision, 98.2% recall, and 98.5% F1 score on the NSL-KDD99 dataset.
Demonstrated MAML's effectiveness in detecting a wide range of cyber threats within IoT environments.

Conclusions:

Meta-learning-based intrusion detection offers a robust solution for building resilient IoT systems.
The proposed MAML framework significantly enhances IDS performance in dynamic IoT settings.
Future research will explore federated meta-learning and real-time deployment for adaptive threat response.