Energy-Efficient Security Solutions for Next-Generation Embedded Systems

The proliferation of embedded systems and the Internet of Things (IoT) has opened up new possibilities for various applications. However, with these advancements come heightened security risks, making IoT security a major concern. Typically, embedded systems operate in resource-constrained environments with low power and area budgets, necessitating security solutions that can adapt to such conditions with minimal overhead. This thesis presents research that demonstrates the implementation of efficient secure solutions for resource-constrained embedded systems across different threat models and applications. Specifically, this work focuses on three broad applications. Firstly, it proposed to improve the security of implantable medical devices through dual-factor authentications that incorporate human responses alongside cryptographic security. Secondly, this thesis also examines the side-channel security vulnerabilities of embedded Neural Network implementations, which have been mitigated by the development of a side-channel secure Neural Network Accelerator with improved defenses. The thesis further explores fault attacks over Neural network implementations, leading to the development of the first ASIC demonstration of a fault-attack-resistant neural engine. Finally, this work delves into anti-counterfeiting in agriculture and develops an interdisciplinary solution that combines materials engineering and computer science. The solution involves the use of silk tags attached to individual seeds, assigning them unique identities using Physical Unclonable Functions. Although each application comes with unique challenges, all solutions prioritize security and are tailored specifically for resource-constrained embedded systems with minimal resource overheads. The findings of this thesis are an attempt towards developing security solutions for resource-constrained embedded systems by integrating appropriate algorithmic and architectural innovation with interdisciplinary solutions.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Maji, Saurav

Advisor dc:contributor.advisor

• Chandrakasan, Anantha P.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151696

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151696