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：随着技术的进步，机器学习在网络安全中的应用日益关键，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，但其高能耗与资源需求限制了应用范围，从而催生了更适合资源受限环境的微型机器学习。微型机器学习广泛应用于智能家居、医疗保健和工业自动化等领域，专注于为小型低功耗设备优化机器学习算法，实现边缘设备上的智能数据处理。本文全面综述了微型机器学习技术面临的常见挑战，如功耗、有限内存和计算约束，并探讨了潜在解决方案，包括能量采集、计算优化技术以及用于隐私保护的迁移学习。同时，本文以电动汽车充电基础设施为典型用例，阐述了微型机器学习在推进网络安全中的应用。研究通过实验案例，在减少延迟和内存占用方面对比了基于微型机器学习与传统机器学习增强电动汽车充电基础设施网络安全的效果，并兼顾了准确性上的小幅权衡。此外，研究在PlatformIO环境下基于ESP32微控制器进行了实际部署，为微型机器学习在电动汽车充电基础设施网络安全中的应用提供了实践评估。