Visible to the public Spotlight on Lablet Research #21 - Principles of Secure BootStrapping for IoTConflict Detection Enabled

Spotlight on Lablet Research #21 -

Principles of Secure BootStrapping for IoT

Lablet: North Carolina State University
Sub-Lablet: Purdue University

This research project, which concluded in March 2021, was motivated by the fact that IoT devices need trust and secure communication--trust between devices and trust between devices and users. Constraints, however, limit options, and deployment scenarios determine resource availability, including power supply, computing resources, and serviceability. The research goal was to develop a lexicon and principles to model the different IoT security bootstrapping scenarios and tools to help developers. The success criteria included being able to see the developed lexicon and develop the most important IoT bootstrapping tool.

The research plan for modeling IoT bootstrapping scenarios had the following steps:

  • Determine how it works today in different application domains
  • Develop a conceptual framework and vocabulary
  • Analyze device interactions from the perspective of a single device
  • Analyze combinations of adversary model, capability, resource, protocols, and security goals
  • Develop a tool to aid developers

The research team, led by Principal Investigator (PI) Ninghui Li of Purdue University, designed an enhanced bootstrapping protocol for Zigbee that prevents a wide range of attacks. Their investigation uncovered a number of critical security and privacy issues in the connection establishment (also known as the "joining") procedure of the Zigbee protocol. To mitigate these issues, the team designed and implemented an enhanced connection establishment procedure. In this solution, they leveraged the existing installation code mechanism to use it as public-key cryptography and combine it with the Elliptic-Curve Diffie-Hellman (ECDH) mechanism to ensure better security and privacy guarantees.

They evaluated their proposed enhancements to the Zigbee protocol, which they proposed to avoid vulnerabilities in Zigbee that were previously identified. The team used ProVerif to verify the correctness of the proposed protocols, and then implemented and deployed the enhanced protocol to evaluate and compare with the Zigbee standard implementations in terms of delay, memory usage, and message size. They found that the enhanced protocol does not introduce extra messages and induces only 3.8% overhead on average for the entire join procedure.

Going beyond their previous work with Zigbee, the team started working on two new topics: one is Connected Vehicle Systems security, and the other is contact tracing. They identified important challenges in connected vehicles, specifically, their keyless (i.e., fob-based) entry systems and on-board diagnostic systems, and found that existing approaches expose a large threat surface that could be exploited to impersonate a vehicle owner, gain control of a vehicle, or steal private information. They also looked at security and privacy concerns in mobile contact tracing apps.

With a focus on studying contact tracing protocols, the team developed a framework to analyze Proximity-based Contact Tracing (PCT) protocols and has identified two main dimensions along with which different designs for PCT protocols can be made.

Additional detail on this project can be found here.