This repository contains the complete workflow for TinyML-based network security for Electric Vehicle Charging Infrastructure (EVCI).
This folder contains the final dataset files used for model training and evaluation:
Final_EVSE_A.csvFinal_EVSE_B.csv
Contains scripts and logs related to model training and hyperparameter tuning.
Includes scripts and results for selecting the most relevant features for each model.
Stores model pruning experiments to optimize efficiency.
All final results, including performance metrics, evaluation reports, and visualizations, are saved in this directory.
This folder contains all files required to deploy and evaluate the trained TinyML models on a Raspberry Pi device.
@ARTICLE{11303937, author={Dehrouyeh, Fatemeh and Shaer, Ibrahim and Nikan, Soodeh and Ajaei, Firouz Badrkhani and Shami, Abdallah}, journal={IEEE Transactions on Network Science and Engineering}, title={TinyML-Enabled Resource-Efficient Framework for Real-Time Network Securiy in Electric Vehicle Charging Networks}, year={2026}, volume={13}, pages={5092-5109}, keywords={Real-time systems;Computational modeling;Biological system modeling;Accuracy;Security;Protocols;Monitoring;Malware;Long short term memory;Intrusion detection;Cybersecurity;electric vehicle (EV);electric vehicle charging infrastructure (EVCI);model compression;optuna;pruning;shapley additive explanations (SHAP);TinyML}, doi={10.1109/TNSE.2025.3645564}}