Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI

TL;DR

This paper introduces a scalable, lightweight IoT security framework combining adaptive curriculum learning with Explainable AI techniques like LIME, achieving high accuracy, robustness, and transparency for edge deployment.

Contribution

It proposes a novel neural network architecture integrated with curriculum learning and XAI, optimized for IoT security with staged learning, quantization, pruning, and ensemble methods.

Findings

Achieved 98% accuracy on CIC-IoV-2024 and CIC-APT-IIoT-2024 datasets.

Demonstrated robustness against noise and low-relevance features.

Ensured edge deployment through quantization and pruning.

Abstract

To address the critical need for secure IoT networks, this study presents a scalable and lightweight curriculum learning framework enhanced with Explainable AI (XAI) techniques, including LIME, to ensure transparency and adaptability. The proposed model employs novel neural network architecture utilized at every stage of Curriculum Learning to efficiently capture and focus on both short- and long-term temporal dependencies, improve learning stability, and enhance accuracy while remaining lightweight and robust against noise in sequential IoT data. Robustness is achieved through staged learning, where the model iteratively refines itself by removing low-relevance features and optimizing performance. The workflow includes edge-optimized quantization and pruning to ensure portability that could easily be deployed in the edge-IoT devices. An ensemble model incorporating Random Forest, XGBoost, and the staged learning base further enhances generalization. Experimental results demonstrate 98% accuracy on CIC-IoV-2024 and CIC-APT-IIoT-2024 datasets and 97% on EDGE-IIoT, establishing this framework as a robust, transparent, and high-performance solution for IoT network security.