Evaluation of CAN Bus Security Challenges

Bozdal, Mehmet; Samie, Mohammad; Aslam, Sohaib; Jennions, Ian K.

doi:10.3390/s20082364

Cited by 126 publications

(53 citation statements)

References 44 publications

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“…Network segmentation, encryption, authentication and intrusion detection. However, although that many actions have been undertaken to solve vulnerability issues, it´s an open research topic [41]. Fig.…”

Section: Vehicle Information Data Exchangementioning

confidence: 99%

Aggregator to Electric Vehicle LoRaWAN Based Communication Analysis in Vehicle-to-Grid Systems in Smart Cities

et al. 2020

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Recently, there has been growing attention to the power grid management due to the increasing concerns on global warming. With the advancement in electric vehicles (EV) industry and the evolution in batteries, EVs become an important contributor to the grid with capability of bidirectional power exchange with the grid. In this context, Vehicle-to-Grid (V2G) systems enable multiple functionalities between EVs and the corresponding aggregator. Thus, reliable, long-range communication capabilities between aggregator and EVs is compulsory. In this paper, wireless channel analysis for aggregator and electrical vehicle communication using Long-Range Wide Area Network (LoRaWAN) technology in V2G is presented, in order to test a low-cost solution with large coverage and reduced power consumption profile. Wireless channel and system-level measurements have been performed in a real urban scenario between EV's charging station in Pamplona (Spain) and a vehicle in motion using LoRaWAN 868 MHz devices. Wireless channel characterization is performed by implementing a full 3D urban scenario model, including elements such as buildings, vehicles, users and urban infrastructure such as lamp posts and benches. By means of in-house developed 3D Ray Launching algorithm with hybrid simulation capabilities, estimations of received power levels, signal to noise ratio and time domain parameters have been obtained, for the complete volume of the scenario under test in dense urban conditions. V2G end to end communication has been validated by implementing an intra-vehicle Controller Area Network-BUS (CAN BUS) data gathering system connected to the vehicle LoRaWAN transceiver and subsequently, to a cloud-based web service. The results show that the accurate deterministic based radio channel analysis enables to optimize the network design of LoRaWAN networks in a vehicular environment, considering inter-vehicular and infrastructure links, enabling scalable, low cost end to end data exchange for the deployment of ancillary V2G services.

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Section: Vehicle Information Data Exchangementioning

confidence: 99%

Aggregator to Electric Vehicle LoRaWAN Based Communication Analysis in Vehicle-to-Grid Systems in Smart Cities

et al. 2020

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“…During the development of the CAN bus protocol, vehicles were considered as isolated objects that did not have a connection with the outside environment [ 9 ]. By design, the CAN bus protocol is plagued with various security issues because of the lack of encryption and authentication requirements [ 10 ]. Therefore, any malicious or hijacked node can cause disastrous accidents and serious financial loss.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Mehedi

Anwar

Rahman

et al. 2021

Sensors

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The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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“…Despite its advantages, CAN bus suffers from many vulnerabilities. The main problem is that a CAN lacks any kind of security mechanism because this was not considered in its design [7]. Attacks on a CAN bus can come from outside, particularly from the On-Board Diagnostics (OBD) [8], or from other wireless interfaces, such as cellular links, Wi-Fi, and Bluetooth [5], [9].…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus

et al. 2021

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Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or to total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem when using a CAN bus protocol for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, in this paper, data analysis and statistical learning techniques are applied. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques proposed in this paper outperform other techniques that have been used previously.

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Evaluation of CAN Bus Security Challenges

Cited by 126 publications

References 44 publications

Aggregator to Electric Vehicle LoRaWAN Based Communication Analysis in Vehicle-to-Grid Systems in Smart Cities

Aggregator to Electric Vehicle LoRaWAN Based Communication Analysis in Vehicle-to-Grid Systems in Smart Cities

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus

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