Abstract
Exploring the intersection of tiny machine learning (TinyML) and cybersecurity, this chapter focuses on the application of lightweight cryptographic algorithms to enhance data security in microcontroller-based systems. It begins with an overview of the unique challenges posed by the resource constraints of Internet of Things (IoT) devices in implementing standard cryptographic protocols. The discussion then moves to state-of-the-art lightweight algorithms that are suitable for TinyML environments, including block ciphers, hash functions, and stream ciphers designed to operate efficiently on limited hardware. Comparative analysis of these algorithms provides insights into their security levels, computational demands, and energy consumption, supported by simulation results and practical implementations. The chapter further addresses the integration of these cryptographic tools into existing TinyML frameworks, exemplifying their real-world applicability and effectiveness in securing TinyML applications against increasingly sophisticated cyberthreats. Code samples are provided to demonstrate the usage of lightweight cryptographic algorithms in TinyML contexts.