TY - GEN
T1 - Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI
AU - Narkedimilli, S.
AU - Makam, S.
AU - Sriram, A. V.
AU - Mallellu, S. Prashanth
AU - Sathvik, M.
AU - Prasad, R. V.
N1 - Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2025
Y1 - 2025
N2 - 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, like LIME, to ensure transparency and adaptability. The proposed model employs a 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 edge IoT devices. An ensemble model incorporating Random Forest, XGBoost, and the staged learning base further enhances generalization. The 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.
AB - 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, like LIME, to ensure transparency and adaptability. The proposed model employs a 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 edge IoT devices. An ensemble model incorporating Random Forest, XGBoost, and the staged learning base further enhances generalization. The 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.
KW - Curriculum Learning
KW - Explainable Artificial Intelligence (XAI)
KW - Intrusion Detection System
KW - IoT Network Security
UR - https://www.scopus.com/pages/publications/105032392267
U2 - 10.1109/FNWF66845.2025.11317635
DO - 10.1109/FNWF66845.2025.11317635
M3 - Conference contribution
SN - 979-8-3315-9194-6
T3 - 2025 IEEE Future Networks World Forum: Beyond Connectivity: 6g for a Sustainable and Intelligent Future, FNWF 2025
BT - Proceedings of the 2025 IEEE Future Networks World Forum (FNWF)
PB - IEEE
T2 - 2025 IEEE Future Networks World Forum (FNWF)
Y2 - 10 November 2025 through 12 November 2025
ER -