Adaptive Multi-Layer Encryption for Enhancing Security in Smart Home IoT Ecosystems

The proliferation of Internet of Things (IoT) devices in smart home environments has introduced significant security challenges, largely due to device heterogeneity, constrained computational resources, and limited native support for robust encryption mechanisms. This study presents an Adaptive Multi-Layer Encryption (AMLE) model designed to enhance security across the device, network, and cloud layers of smart home IoT ecosystems. The proposed model integrates lightweight encryption mechanisms at the device layer, machine learning-based anomaly detection at the network layer, and strong cryptographic protection combined with Attribute-Based Access Control (ABAC) at the cloud layer. Evaluation was conducted in a controlled, simulated environment to assess functional correctness, security behavior under representative threat scenarios, and system performance. Results demonstrate that the AMLE framework is capable of detecting and responding to simulated unauthorized access attempts, anomalous traffic patterns associated with botnet-like behavior, and data exfiltration scenarios, while maintaining operational performance suitable for typical smart home use cases. The Isolation Forest algorithm, configured with a contamination threshold of 0.05, successfully identified deviations from baseline traffic behavior and triggered policy-driven security responses. The findings indicate that AMLE provides a practical reference framework for implementing adaptive, layered security controls in smart home IoT environments, balancing security requirements with operational constraints.

@article{Machuki2026,
  title     = {Adaptive Multi-Layer Encryption for Enhancing Security in Smart Home IoT Ecosystems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Dancan Obuya Machuki and Kennedy Ronoh},
  doi       = {10.14569/IJACSA.2026.0170473},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170473}
}