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

arXiv: https://arxiv.org/abs/2311.08000 · HTML: https://mlsanity.com/paper/2311.08000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups](https://mlsanity.com/paper/1605.06489/) (2016)
- [CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data](https://mlsanity.com/paper/1906.02492/) (2019)
- [A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset](https://mlsanity.com/paper/2012.14600/) (2020)
- [Cyberattacks and Countermeasures For In-Vehicle Networks](https://mlsanity.com/paper/2004.10781/) (2020)
- [Local Critic Training for Model-Parallel Learning of Deep Neural Networks](https://mlsanity.com/paper/2102.01963/) (2021)
- [Cloaking the Clock: Emulating Clock Skew in Controller Area Networks](https://mlsanity.com/paper/1710.02692/) (2017)

## Abstract (arXiv)

With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area network (CAN) has many potential security hazards, resulting in higher generalization capability and lighter security requirements for intrusion detection systems to ensure safety. Among intrusion detection technologies, methods based on deep learning work best without prior expert knowledge. However, they all have a large model size and usually rely on large computing power such as cloud computing, and are therefore not suitable to be installed on the in-vehicle network. Therefore, we explore computational resource allocation schemes in in-vehicle network and propose a lightweight parallel neural network structure, LiPar, which achieve enhanced generalization capability for identifying normal and abnormal patterns of in-vehicle communication flows to achieve effective intrusion detection while improving the utilization of limited computing resources. In particular, LiPar adaptationally allocates task loads to in-vehicle computing devices, such as multiple electronic control units, domain controllers, computing gateways through evaluates whether a computing device is suitable to undertake the branch computing tasks according to its real-time resource occupancy. Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security. Furthermore, with only the common multi-dimensional branch convolution networks for detection, LiPar can have a high potential for generalization in spatial and temporal feature fusion learning.