A Robust Secure and Energy-Aware Cross-Layer Framework for IoT Networks

Abstract

The dual challenges of energy constraints and multi-layered cyber threats must be addressed in order to secure Internet of Things (IoT) environments. To overcome the above problems, we propose a secure and energy-aware cross layer framework for IoT networks. Our framework is based on the combined role-based access control, machine learning based anomaly detection, and lightweight encryption. We explore context-aware defenses that can remain scalable and energy efficient while dynamically adapting to changing attack vectors. The performance of the proposed framework is evaluated using real hardware (Z1 and EXP430F5438 motes) after being validated by simulations on the Cooja and NS-3 platforms. The results demonstrate up to 30% energy savings over AES while preserving high detection performance for both active and passive threat models and over 95% packet delivery. These results highlight the necessity of adaptive, multi-layer strategies for contemporary IoT deployments and show that a secure, scalable, and energy conscious IoT design is feasible.