  As technology advances, the use of Machine Learning (ML) in cybersecurity is
becoming increasingly crucial to tackle the growing complexity of cyber
threats. While traditional ML models can enhance cybersecurity, their high
energy and resource demands limit their applications, leading to the emergence
of Tiny Machine Learning (TinyML) as a more suitable solution for
resource-constrained environments. TinyML is widely applied in areas such as
smart homes, healthcare, and industrial automation. TinyML focuses on
optimizing ML algorithms for small, low-power devices, enabling intelligent
data processing directly on edge devices. This paper provides a comprehensive
review of common challenges of TinyML techniques, such as power consumption,
limited memory, and computational constraints; it also explores potential
solutions to these challenges, such as energy harvesting, computational
optimization techniques, and transfer learning for privacy preservation. On the
other hand, this paper discusses TinyML's applications in advancing
cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a
representative use case. It presents an experimental case study that enhances
cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms
of reduced delay and memory usage, with a slight trade-off in accuracy.
Additionally, the study includes a practical setup using the ESP32
microcontroller in the PlatformIO environment, which provides a hands-on
assessment of TinyML's application in cybersecurity for EVCI.
