SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion
  Detection and Classification
Xuemei Li Huirong Fu
Numerous studies have proved their effective strength in detecting Control
Area Network (CAN) attacks. In the realm of understanding the human semantic
space, transformer-based models have demonstrated remarkable effectiveness.
Leveraging pre-trained transformers has become a common strategy in various
language-related tasks, enabling these models to grasp human semantics more
comprehensively. To delve into the adaptability evaluation on pre-trained
models for CAN intrusion detection, we have developed two distinct models:
CAN-SecureBERT and CAN-LLAMA2. Notably, our CAN-LLAMA2 model surpasses the
state-of-the-art models by achieving an exceptional performance 0.999993 in
terms of balanced accuracy, precision detection rate, F1 score, and a
remarkably low false alarm rate of 3.10e-6. Impressively, the false alarm rate
is 52 times smaller than that of the leading model, MTH-IDS (Multitiered Hybrid
Intrusion Detection System). Our study underscores the promise of employing a
Large Language Model as the foundational model, while incorporating adapters
for other cybersecurity-related tasks and maintaining the model's inherent
language-related capabilities.