A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks

arXiv:2608.02274 · cs.CR, cs.LG · Submitted 2026-08-03 · Read on arXiv

Li Yang

cs.CR, cs.LG

Submitted: 2026-08-03

Comments: To appear in the Proceedings of the 2026 IEEE Global Communications Conference (GLOBECOM 2026). Code is available at: https://github.com/LiYangHart/MOO-NSGA-III-AutoML-based-Intrusion-Detection-System

Code: https://github.com/LiYangHart/MOO-NSGA-III-AutoML-based-Intrusion-Detection-System

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Feature Selection — 15× in this paper · explained in 4 episodes
Accuracy — 9× in this paper · explained in 1 episode
Feature Importance — 9× in this paper · explained in 2 episodes
Hyperparameters — 6× in this paper · explained in 2 episodes
Genetic Algorithm — 5× in this paper
Machine Learning — 4× in this paper
Benchmark — 3× in this paper · explained in 2 episodes
Internet of Things — 3× in this paper
Machine Learning (ML) — 3× in this paper · explained in 2 episodes
Multi-objective Optimization — 3× in this paper · explained in 5 episodes
F1 Score — 2× in this paper · explained in 4 episodes
Augmentation — 1× in this paper · explained in 2 episodes
Challenges of the Open Charge Point Protocol — 1× in this paper
Computational overhead — 1× in this paper · explained in 2 episodes
Core — 1× in this paper · explained in 2 episodes
Evolutionary Search — 1× in this paper · explained in 3 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
Single-Objective Optimization — 1× in this paper
Transfer Learning — 1× in this paper · explained in 16 episodes

Terminology

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