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.

翻译：随着技术的发展，机器学习在网络安全领域的应用日益关键，以应对日益复杂的网络威胁。传统机器学习模型虽能增强网络安全，但其高能耗与高资源需求限制了应用场景，由此催生了更适合资源受限环境的微型机器学习（TinyML）。TinyML广泛应用于智能家居、医疗健康和工业自动化等领域，其核心在于优化面向小型低功耗设备的机器学习算法，实现边缘设备端的智能数据处理。本文系统综述了TinyML技术面临的共性挑战（如功耗、有限内存与计算约束），并探讨了潜在解决方案（如能量采集、计算优化技术与隐私保护迁移学习）。同时，以电动汽车充电基础设施（EVCI）为代表案例，本文讨论了TinyML在推进其网络安全中的应用。研究通过对比实验案例，展示了TinyML在EVCI网络安全增强中的效果——与传统机器学习相比，该方法在降低延迟与内存占用的同时，仅牺牲了少量精度。此外，研究基于ESP32微控制器在PlatformIO环境中搭建实践平台，为TinyML在EVCI网络安全中的应用提供了实操评估。