An Explainable and Resource-Efficient Deep Learning Framework for Secure Data Transmission in IoT Edge Networks.

Publication Details

Publication Date

10/10/2026

Volume / Issue

Vol 1, Issue 5 (2026)

Article No.

010

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-HWW3N6JF

Received

14 Jun 2026

Views

22

Downloads

16

Abstract

Resource-constrained environments such as Internet of Things (IoT) and edge networks face increasing cybersecurity threats while lacking sufficient computational capacity for traditional security mechanisms. Existing deep learning-based intrusion detection systems offer high accuracy but suffer from high complexity and lack of interpretability, limiting their deployment in low-power environments. This paper proposes a lightweight Explainable Artificial Intelligence (XAI) framework for secure data transmission that integrates an optimized autoencoder-based anomaly detection model with SHAP-based interpretability. The system is evaluated using UNSW-NB15 and CICIDS2017 datasets under constrained computational settings. Experimental results show that the proposed framework achieves 97.8% accuracy, 96.4% F1-score, and reduces computational overhead by 32% compared to conventional deep learning models. Additionally, the explainability module identifies critical features influencing attack detection, improving trust and transparency. These results are obtained using benchmark datasets commonly adopted in intrusion detection research. The framework provides a scalable, efficient, and interpretable solution suitable for real-time deployment in IoT and edge networks.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.