Abstract
With the proliferation of intelligent connected vehicles, the Controller Area Network (CAN) bus, as the backbone of in-vehicle communication, is vulnerable to cyberattacks due to lack of authentication and encryption. Existing Intrusion Detection Systems (IDS) exhibit limitations in addressing data imbalance, complex attack recognition, and resource-constrained deployment. This paper proposes a lightweight intrusion detection system based on multi-objective optimization, termed MOO-IDS. The system integrates a Hybrid Deep Restricted Boltzmann Machine Generator (HyDRBM-Gen) to mitigate attack sample scarcity and class imbalance in vehicular network data. To address targeted ID fabrication and masquerade attacks, two novel features, ID Frequency (IDF) and ID-Data Correlation (IDC), are introduced. An Adaptive-Enhanced Non-Dominated Sorting Dung Beetle Optimizer (AE-NSDBO) optimizes XGBoost hyperparameters to achieve an optimal balance among F1-score, model size, and inference latency, enabling the optimized XGBoost to accomplish intrusion detection. Experimental results on the real-world dataset Real ORNL Automotive Dynamometer (ROAD) demonstrate that MOO-IDS achieves outstanding performance, with a weighted-average F1-score of 0.9907, a model size of only 0.199 MB, and substantially reduced inference latency. The model also performs excellently on the Car Hacking and Survival Analysis datasets. This study provides a high-precision, lightweight, and strongly generalizable multi-class intrusion detection model for resource-constrained vehicular environments.