On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

TL;DR

This paper reviews TinyML challenges and solutions, and demonstrates its application in enhancing cybersecurity for Electric Vehicle Charging Infrastructures through an experimental case study with practical implementation.

Contribution

It introduces a practical TinyML-based cybersecurity solution for EVCI, highlighting its advantages and trade-offs compared to traditional ML methods.

Findings

Reduced delay and memory usage with TinyML

Slight decrease in accuracy compared to traditional ML

Successful implementation on ESP32 microcontroller

Abstract

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.