Privacy-preserving Robotic-based Multi-factor Authentication Scheme for Secure Automated Delivery System

TL;DR

This paper introduces a privacy-preserving multi-factor authentication scheme for robotic delivery systems, addressing security threats like impersonation and adversarial attacks with a novel transformer-based defender, supported by formal analysis and real-world implementation.

Contribution

It presents the first transformer-based audio-visual fusion defender against machine learning attacks in robotic delivery systems, along with a formal security analysis and practical deployment.

Findings

The scheme effectively defends against impersonation and MITM attacks.

The transformer-based defender provides resilience to adversarial samples.

Real-world implementation shows acceptable computation and energy costs.

Abstract

Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile transport problem, in particular, contributes significantly to the expenditure of human resources in major companies. Robot-based delivery systems have emerged as a potential solution for last-mile delivery to address this challenge. However, robotic delivery systems still face security and privacy issues, like impersonation, replay, man-in-the-middle attacks (MITM), unlinkability, and identity theft. In this context, we propose a privacy-preserving multi-factor authentication scheme specifically designed for robot delivery systems. Additionally, AI-assisted robotic delivery systems are susceptible to machine learning-based attacks (e.g. FGSM, PGD, etc.). We introduce the \emph{first} transformer-based audio-visual fusion defender to tackle this issue, which effectively provides resilience against adversarial samples. Furthermore, we provide a rigorous formal analysis of the proposed protocol and also analyse the protocol security using a popular symbolic proof tool called ProVerif and Scyther. Finally, we present a real-world implementation of the proposed robotic system with the computation cost and energy consumption analysis. Code and pre-trained models are available at: https://drive.google.com/drive/folders/18B2YbxtV0Pyj5RSFX-ZzCGtFOyorBHil