Viden

Cho, Kyong-Tak; Shin, Kang G.

doi:10.1145/3133956.3134001

Cited by 160 publications

(7 citation statements)

References 3 publications

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“…In the literature, three attack scenarios for in-vehicle networks are documented: suspension, fabrication, and masquerade attacks [8,9,25,26], which are considered in our evaluation. In addition, we introduce and evaluate spectra on an attack of a more stealthy nature, which we refer to as the conquest attack, to highlight the detection capabilities of our approach.…”

Section: Attack Scenariosmentioning

confidence: 99%

Spectra

Aoudi

Nowdehi

Almgren

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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Nowadays, vehicles have complex in-vehicle networks that have recently been shown to be increasingly vulnerable to cyber-attacks capable of taking control of the vehicles, thereby threatening the safety of the passengers. Several countermeasures have been proposed in the literature in response to the arising threats, however, hurdle requirements imposed by the industry is hindering their adoption in practice. In this paper, we propose spectra, a datadriven anomaly-detection mechanism that is based on spectral analysis of CAN-message payloads. Spectra does not abide by the strict specifications predefined for every vehicle model and addresses key real-world deployability challenges.

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Section: Attack Scenariosmentioning

confidence: 99%

Spectra

Aoudi

Nowdehi

Almgren

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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“…In practice, however, square-wave signals are characterized by rise and fall times, indicating the time required for a signal to reach its target value. Among others, these are influenced by the capacitances and inductances of the circuits and by the power supply of the ECU respectively the transceiver [10]. The primary voltage source used is a 12 V battery for passenger cars and 24 V for trucks.…”

Section: B Cause Of the Signal Characteristicsmentioning

confidence: 99%

“…The IDS extension Voltage-based attacker identification (Viden) [10] works only on the basis of the voltage level, whose goal is to identify the attacking ECU after an intrusion has been detected by a high-level IDS. The system generates a model based on the average voltage level of dominant bits of the ECUs, collected from multiple frames.…”

Section: Related Workmentioning

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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“…In addition to detecting anomalous behavior, methods to identify which malicious ECU sent a false message in the network have been proposed from 2014 [60]- [62]. Using these techniques, which ECU is compromised or which ECU is illegally installed in the network can be identified and the target ECU can immediately be isolated.…”

Section: Implementing Anomaly Detection In In-vehicle Networkmentioning

confidence: 99%

An Overview of Cyber Security for Connected Vehicles

Takahashi

2018

IEICE Trans. Inf. & Syst.

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The demand for and the scope of connected services have rapidly grown and developed in many industries such as electronic appliances, robotics, and industry automation. In the automotive field, including connected vehicles, different types of connected services have become available and they provide convenience and comfort with users while yielding new business opportunities. With the advent of connected vehicles, the threat of cyber attacks has become a serious issue and protection methods against these attacks are urgently needed to provide safe and secure connected services. From 2017, attack methods have become more sophisticated through different attack surfaces attached to navigation systems and telematics modules, and security requirements to circumvent such attacks have begun to be established. Individual threats have been addressed previously; however, there are few reports that provide an overview of cyber security related to connected vehicles. This paper gives our perspective on cyber security for connected vehicles based on a survey of recent studies related to vehicle security. To introduce these studies, the environment surrounding connected vehicles is classified into three categories: inside the vehicle, communications between the back-end systems and vehicles, and the back-end systems. In each category, this paper introduces recent trends in cyber attacks and the protection requirements that should be developed for connected services. We show that the overall security covering the three categories must be considered because the security of the vehicle is jeopardized even if one item in the categories is not covered. We believe that this paper will further contribute to development of all service systems related to connected vehicles including autonomous vehicles and to the investigation into cyber security against these attacks.

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Viden

Cited by 160 publications

References 3 publications

Spectra

Spectra

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

An Overview of Cyber Security for Connected Vehicles

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