Simple

Foruhandeh, Mahsa; Man, Yanmao; Gerdes, Ryan; Li, Ming; Chantem, Thidapat

doi:10.1145/3359789.3359834

Cited by 54 publications

(3 citation statements)

References 26 publications

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“…The SIMPLE dataset [ 37 ] is composed of publicly available data obtained by capturing Controller Area Network (CAN) messages from two vehicles: a 2016 Nissan Sentra and a 2011 Subaru Outback. The data acquisition utilized a Tektronix DPO 3012 oscilloscope connected through the Onboard Diagnostics II (OBD-II) port.…”

Section: Intra-vehicular Communication Datasetsmentioning

confidence: 99%

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

Haddaji,

Ayed,

Chaari Fourati

et al. 2024

Sensors

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Vehicular networks have become a critical component of modern transportation systems by facilitating communication between vehicles and infrastructure. Nonetheless, the security of such networks remains a significant concern, given the potential risks associated with cyberattacks. For this purpose, artificial intelligence approaches have been explored to enhance the security of vehicular networks. Using artificial intelligence algorithms to analyze large datasets can enable the early identification and mitigation of potential threats. However, developing and testing effective artificial-intelligence-based solutions for vehicular networks necessitates access to diverse datasets that accurately capture the various security challenges and attack scenarios in this context. In light of this, the present survey comprehensively examines the vehicular network environment, the associated security issues, and existing datasets. Specifically, we begin with a general overview of the vehicular network environment and its security challenges. Following this, we introduce an innovative taxonomy designed to classify datasets pertinent to vehicular network security and analyze key features of these datasets. The survey concludes with a tailored guide aimed at researchers in the vehicular network domain. This guide offers strategic advice on selecting the most appropriate datasets for specific research scenarios in the field.

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Section: Intra-vehicular Communication Datasetsmentioning

confidence: 99%

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

Haddaji,

Ayed,

Chaari Fourati

et al. 2024

Sensors

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“…Simple [17] creates an average symbol like Volt-ageIDS [12] and therefore also utilizes comprehensive signal characteristics for sender identification. The difference to previous methods lies in the fact that the average symbol is used as a direct input for the identification via a distance metric.…”

Section: Related Workmentioning

confidence: 99%

“…Scission [34] is comparable to the VoltageIDS, but operates with a lower sample rate of 20 MS/s, resulting in a memory requirement of 3.36 kB. Simple [17] samples initially the whole frame with 50 MS/s, including the bit fields before the payload in order to extract the identifier. Since this can easily be avoided by using the CAN controller, we also assume 86 bits here, which corresponds to a memory requirement of 8.4 kB.…”

Section: Resource Requirementsmentioning

confidence: 99%

EASI: Edge-Based Sender Identification on Resource-Constrained Platforms for Automotive Networks

Kneib

Schell

Huth

2020

Proceedings 2020 Network and Distributed System Security Symposium

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In vehicles, internal Electronic Control Units (ECUs) are increasingly prone to adversarial exploitation over wireless connections due to ongoing digitalization. Controlling an ECU allows an adversary to send messages to the internal vehicle bus and thereby to control various vehicle functions. Access to the Controller Area Network (CAN), the most widely used bus technology, is especially severe as it controls brakes and steering. However, state of the art receivers are not able to identify the sender of a frame. Retrofitting frame authenticity, e.g. through Message Authentication Codes (MACs), is only possible to a limited extent due to reduced bandwidth, low payload and limited computational resources. To address this problem, observation in analog differences of the CAN signal was proposed to determine the actual sender. Some of the prior approaches exhibit good identification and detection rates, however require high sampling rates and a high computing effort. With EASI we significantly reduce the required resources and at the same time show increased identification rates of 99.98% by having no false positives in a prototype structure and two series production vehicles. In comparison to the most lightweight approach so far, we have reduced the memory footprint and the computational requirements by a factor of 168 and 142, respectively. In addition, we show the feasibility of EASI and thus demonstrate for the first time that voltage-based sender identification is realizable using comprehensive signal characteristics on resource-constrained platforms. Due to the lightweight design, we achieved a classification in under 100 µs with a training time of 2.61 seconds. We also showed the ability to adapt the system to incremental signal changes during operation. Since cost effectiveness is of utmost importance in the automotive industry due to high production volumes, the achieved improvements are significant and necessary to realize sender identification.

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CANSentry: Securing CAN-Based Cyber-Physical Systems against Denial and Spoofing Attacks

Humayed

Lin

et al. 2020

Computer Security – ESORICS 2020

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Simple

Cited by 54 publications

References 26 publications

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

EASI: Edge-Based Sender Identification on Resource-Constrained Platforms for Automotive Networks

CANSentry: Securing CAN-Based Cyber-Physical Systems against Denial and Spoofing Attacks

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