controller area network
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2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zi-An Zhao ◽  
Yu Sun ◽  
Dawei Li ◽  
Jian Cui ◽  
Zhenyu Guan ◽  
...  

Intravehicular communication relies on controller area network (CAN) protocol to deliver messages and instructions among different electronic control units (ECU). Unfortunately, inherent defects in CAN include the absence of confidentiality and integrity mechanism, enabling adversaries to launch attacks from wired or wireless interfaces. Although various CAN cryptographic protocols have been proposed for entity authentication and secure communication, the redundancy in the key establishment phase weakens their availability in large-scale CAN. In this paper, we propose a scalable security protocol suite for intravehicular networks and reduce the communication costs significantly. A new type of attack, suspension attack, is identified for the existing protocols and mitigated in our protocol by leveraging a global counter scheme. We formally verify the security properties of the proposed protocol suite through the AVISPA tool. The simulation results indicate that the communication and computation efficiency are improved in our protocol.


2021 ◽  
Author(s):  
Mert D. Pesé ◽  
Jay W. Schauer ◽  
Junhui Li ◽  
Kang G. Shin

2021 ◽  
Author(s):  
Sena Hounsinou ◽  
Mark Stidd ◽  
Uchenna Ezeobi ◽  
Habeeb Olufowobi ◽  
Mitra Nasri ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Anil Kumar Biswal ◽  
Debabrata Singh ◽  
Binod Kumar Pattanayak ◽  
Debabrata Samanta ◽  
Shehzad Ashraf Chaudhry ◽  
...  

Nowadays, the power consumption and dependable repeated data collection are causing the main issue for fault or collision in controller area network (CAN), which has a great impact for designing autonomous vehicle in smart cities. Whenever a smart vehicle is designed with several sensor nodes, Internet of Things (IoT) modules are linked through CAN for reliable transmission of a message for avoiding collision, but it is failed in communication due to delay and collision in communication of message frame from a source node to the destination. Generally, the emerging role of IoT and vehicles has undoubtedly brought a new path for tomorrow’s cities. The method proposed in this paper is used to gain fault-tolerant capability through Probabilistic Automatic Repeat Request (PARQ) and also Probabilistic Automatic Repeat Request (PARQ) with Fault Impact (PARQ-FI), in addition to providing optimal power allocation in CAN sensor nodes for enhancing the performance of the process and also significantly acting a role for making future smart cities. Several message frames are needed to be retransmitted on PARQ and fault impact (PARQ-FI) calculates the message with a response probability of each node.


Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2442
Author(s):  
Cheongmin Ji ◽  
Taehyoung Ko ◽  
Manpyo Hong

In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control units (TCUs). This leads to an attack vector in which attackers can control vehicles remotely once they gain access to in-vehicle networks (IVNs) and can discover the formats of important messages. Although the format information is kept secret by car manufacturers, CAN is vulnerable, since payloads are transmitted in plain text. In contrast, the secrecy of message formats inhibits IVN security research by third-party researchers. It also hinders effective security tests for in-vehicle networks as performed by evaluation authorities. To mitigate this problem, a method of reverse-engineering CAN payload formats is proposed. The method utilizes classification algorithms to predict signal boundaries from CAN payloads. Several features were uniquely chosen and devised to quantify the type-specific characteristics of signals. The method is evaluated on real-world and synthetic CAN traces, and the results show that our method can predict at least 10% more signal boundaries than the existing methods.


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