# Linking Received Packet to the Transmitter Through Physical-Fingerprinting of Controller Area Network

arXiv: https://arxiv.org/abs/1801.09011 · HTML: https://mlsanity.com/paper/1801.09011/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks](https://mlsanity.com/paper/2107.05172/) (2021)
- [SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning](https://mlsanity.com/paper/2101.02780/) (2021)
- [ECU Identification using Neural Network Classification and Hyperparameter Tuning](https://mlsanity.com/paper/2208.10651/) (2022)

## Abstract (arXiv)

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time- and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.