LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

Documenti analoghi

• STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning
  di: Wang, Kai, et al.
  Pubblicazione: (2023)
• FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detection
  di: S, Devika, et al.
  Pubblicazione: (2025)
• FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
  di: S, Devika, et al.
  Pubblicazione: (2025)
• Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection
  di: Dong, Huiyao, et al.
  Pubblicazione: (2025)
• Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
  di: Yagiz, Muhammet Anil, et al.
  Pubblicazione: (2024)