Linking received packet to the transmitter through physical-fingerprinting of controller area network

Abstract

Abstract-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.

Static analysis of Android Auto infotainment and on‐board diagnostics II apps

“…However, the CAN is nowadays connected to the driver and the external world through smartphones and tablets plugged in through Bluetooth or USB ports. This paves the way to security attacks to the car and to privacy leaks of the transferred data, as Checkoway et al, 8 Koscher et al, 6 and Avatefipour et al 40 show. Such articles exemplify how an attacker can get complete control over the vehicle's systems 8 .…”

Identify a Spoofing Attack on an In-Vehicle CAN Bus Based on the Deep Features of an ECU Fingerprint Signal

“…However, the proposed IDS did not attempt to utilize features of the CAN message time series and was therefore only able to authenticate ECUs sending periodic CAN messages and not those that send periodic messages, which are likely spoofing frames. In [4,5], the classification of electrical CAN signal using a support vector machine (SVM), a neural network (NN), and a bagged decision tree (BDT) was investigated. In [4], by comparing time and frequency domain features of physical CAN signal with an extended CAN ID field, an even lower average misclassification rate at 0.36% was reported.…”

“…In [4,5], the classification of electrical CAN signal using a support vector machine (SVM), a neural network (NN), and a bagged decision tree (BDT) was investigated. In [4], by comparing time and frequency domain features of physical CAN signal with an extended CAN ID field, an even lower average misclassification rate at 0.36% was reported.…”

“…While a lot of work has been done on ensuring the quality, reliability, and safety of the autonomous vehicle, less attention has been given to protecting them from malicious attacks. Since the CAN bus is a core component of in-vehicle communication, and the security weakness of the CAN bus design is intrinsic, adversaries can typically break the CAN bus to attack a vehicle or take full control of the ECUs by injecting spoofing messages [4,5].…”

Transmitter Classification with Supervised Deep Learning

“…For this reason, artificial impairments are added to the signal and increase classification accuracy. Still in the same context, the work in [1] deals with wired communications to increase security and prevent intrusion of malicious systems in the network inside a car.…”