On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami
TL;DR
This paper surveys TinyML as a scalable solution for cybersecurity on resource-constrained edge devices, arguing that on-device inference reduces latency, bandwidth, and privacy risks compared to cloud-based ML. It documents a comprehensive case study applying TinyML to Electric Vehicle Charging Infrastructure (EVCI) security, including an ESP32-based implementation and a CICIDS2017-derived dataset. Key contributions include a structured review of TinyML challenges, a toolbox of potential solutions and libraries, and an experimental comparison showing TinyML dramatically reduces inference time and memory while preserving accuracy relative to traditional ML. The work demonstrates the practical viability of TinyML for IDS in EVCI and outlines concrete directions—such as federated and online learning, XAI, and standardization—that can accelerate real-world adoption in smart charging ecosystems.
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.