# On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

arXiv: https://arxiv.org/abs/2404.16894 · HTML: https://mlsanity.com/paper/2404.16894/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 130 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [Spear Phishing With Large Language Models](https://mlsanity.com/paper/2305.06972/) (2023)
- [Visual Wake Words Dataset](https://mlsanity.com/paper/1906.05721/) (2019)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions](https://mlsanity.com/paper/2106.09527/) (2021)
- [Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots](https://mlsanity.com/paper/2205.05748/) (2022)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach](https://mlsanity.com/paper/2012.13604/) (2020)
- [TinyFedTL: Federated Transfer Learning on Tiny Devices](https://mlsanity.com/paper/2110.01107/) (2021)
- [Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform](https://mlsanity.com/paper/2111.00601/) (2021)
- [Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning](https://mlsanity.com/paper/2104.08553/) (2021)
- [CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment](https://mlsanity.com/paper/2307.12149/) (2023)
- [Securing the Electric Vehicle Charging Infrastructure](https://mlsanity.com/paper/2105.02905/) (2021)
- [Machine Learning-Enabled Cyber Attack Prediction and Mitigation for EV Charging Stations](https://mlsanity.com/paper/2208.10644/) (2022)
- [Investigating the Security of EV Charging Mobile Applications As an Attack Surface](https://mlsanity.com/paper/2211.10603/) (2022)
- [Battery-Less LoRaWAN Communications using Energy Harvesting: Modeling and Characterization](https://mlsanity.com/paper/2402.07528/) (2024)

## Built on by (most cited first, 9 of 9)

- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure](https://mlsanity.com/paper/2503.14799/) (2025)
- [Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data](https://mlsanity.com/paper/2501.06099/) (2025)
- [Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics](https://mlsanity.com/paper/2509.12233/) (2025)
- [Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices](https://mlsanity.com/paper/2509.01592/) (2025)
- [An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems](https://mlsanity.com/paper/2509.01599/) (2025)
- [Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions](https://mlsanity.com/paper/2604.06411/) (2026)
- [A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks](https://mlsanity.com/paper/2608.02274/) (2026)
- [An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2609.23097/) (2026)

## Abstract (arXiv)

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.