# LCSMC-Net: Lightweight CAN Intrusion Detection via Separable Multiscale Convolution and Attention

## TL;DR

LCSMC-Net is a lightweight AI model for detecting car network intrusions, designed to work efficiently on vehicle hardware.

## Contribution

The paper introduces LCSMC-Net, a novel ultra-lightweight neural architecture for CAN intrusion detection with minimal computational requirements.

## Key findings

- LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs.
- The model meets real-time constraints of automotive embedded systems.
- It outperforms existing solutions in both accuracy and efficiency.

## Abstract

The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear O(N) complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution.