# Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement

## TL;DR

This paper enhances security in NDN-IoT networks by integrating physical-layer device-specific radio-frequency features into the authentication process, significantly improving efficiency, robustness, and accuracy while reducing computational costs.

## Contribution

It introduces a novel lightweight PHY-ID-based authentication scheme integrated with NDN signatures for MEC-enabled IoT, addressing resource constraints and security challenges.

## Key findings

- Authentication time is significantly reduced.
- Robustness against impersonation attacks is improved.
- Authentication accuracy and differentiation rate are increased.

## Abstract

Recent literature has demonstrated the improved data discovery and delivery efficiency gained through applying named data networking (NDN) to a variety of information-centric Internet of things (IoT) applications. However, from a data security perspective, the development of NDN-IoT raises several new authentication challenges. In particular, NDN-IoT authentication may require per-packet-level signatures, thus imposing intolerably high computational and time costs on the resource-poor IoT end devices. This paper proposes an effective solution by seamlessly integrating the lightweight and unforgeable physical-layer identity (PHY-ID) into the existing NDN signature scheme for the mobile edge computing (MEC)-enabled NDN-IoT networks. The PHY-ID generation exploits the inherent signal-level device-specific radio-frequency imperfections of IoT devices, including the in-phase/quadrature-phase imbalance, and thereby avoids adding any implementation complexity to the constrained IoT devices. We derive the offline maximum entropy-based quantization rule and propose an online two-step authentication scheme to improve the accuracy of the authentication decision making. Consequently, a cooperative MEC device can securely execute the costly signing task on behalf of the authenticated IoT device in an optimal manner. The evaluation results demonstrate 1) elevated authentication time efficiency, 2) robustness to several impersonation attacks including the replay attack and the computation-based spoofing attack, and 3) increased differentiation rate and correct authentication probability by applying our integration design in MEC-enabled NDN-IoT networks.