Securing Electric Vehicle Charging Infrastructure: Attack Identification Using Machine Learning

1. Conducted multi-class classification tasks to identify distinct attack types using various machine learning algorithms.
2. Evaluated the use of Hardware Performance Counters (HPC) and kernel events as features, both individually and in combination, to compare and analyze the performance of these algorithms.
3. Extensive experiments using ten-fold cross-validation demonstrated that Random Forest based machine learning model achieved the highest overall accuracy of 93.4%.
4. Attack classes comprising more than 20% of the samples attain nearly 100% accuracy, while classes with less than 3% samples tend to underperform.