CICEV2025: Dataset and simulator for scalable attack detection with multi-GPU and skip-factor optimization

CICEV2025 is a high-fidelity simulator and dataset that reproduces ISO 15,118 traffic among electric vehicles, charging stations, and grid servers, encompassing one benign scenario and eight variants of false-authentication attacks. The framework supports integrated simulation and detection for EV charging infrastructure by combining protocol-compliant traffic generation, system-level metric collection, and scalable deep learning-based intrusion detection. In addition to full packet captures, CICEV2025 logs lightweight CPU performance counters using perf , providing host-level context unavailable in existing benchmarks such as KDD99, UNSW-NB15, CICIoMT2024, and CIC-IIoT-2025. A deep neural network is trained for nine-class classification to distinguish between the benign scenario and eight attack variants. Hyperparameters are exhaustively explored using a memory-aware grid search across multiple GPUs. To accelerate this process, an adaptive skip-factor sampler reduces the training dataset by up to 99.5% during hyperparameter optimization while maintaining the validation-weighted F 1 score within 1% of the baseline. While binary detection indicates only the presence of an intrusion, the proposed multiclass model identifies the specific attack variant, enabling context-aware mitigation and targeted forensic responses in real-world EV charging environments. The proposed pipeline achieves multiclass F 1 scores of up to 0.87 on CICEV2025, 0.99 on KDD99, and 0.85 on UNSW-NB15, along with 0.99 accuracy on both CICIoMT2024 and CIC-IIoT-2025. In binary mode, F 1 scores range from 0.93 to 0.99 across all benchmarks, with both CICEV2025 and KDD99 achieving the maximum score. These results demonstrate that realistic EV charging attacks can be accurately and robustly detected, even after aggressive data reduction. Furthermore, the developed multi-GPU optimization pipeline delivers significant speedups, paving the way for large-scale, real-time deployment in modern electric vehicle infrastructures.