An Automotive Signal-Layer Security and Trust-Boundary Identification Approach

Macher, Georg; Sporer, Harald; Brenner, Eugen; Kreiner, Christian

doi:10.1016/j.procs.2017.05.317

Cited by 10 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In that work, we also provide an evaluation of available analysis methods and a review of recommended threat analysis methods. In Macher et al, 25 we investigate systematic approaches to support the identification of trust boundaries and attack vectors for the safety‐ and cybersecurity‐related aspects of complex automotive systems. In Macher et al and Riel et al, 4,26 we proposed a structured method to integrate security and safety engineering in the existing Automotive SPICE context.…”

Section: Related Workmentioning

confidence: 99%

Towards a security‐driven automotive development lifecycle

Dobaj

Macher

Ekert³

et al. 2021

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

Cybersecurity has become one of the most crucial challenges in the automotive development lifecycle. The upcoming ISO/SAE 21434 standard provides only a generic framework that is insufficient to derive concrete design methods. This article proposes an actionable cybersecurity development lifecycle model that provides concrete action and work product guidance aligned with the ISO/SAE 21434 and Automotive SPICE® extension for cybersecurity. The model has been inspired by action research in “next” industry practice pilot projects, which ensures that it is actionable. It has been augmented by insights gained from literature research in cybersecurity development for embedded systems. The proposed lifecycle model complements the ISO/SAE 21434 standard and provides the basis for the company‐specific process and practice specifications. It has been validated through the integration of cybersecurity‐related aspects in an electric power steering system. A core characteristic of the model is the central role of threat modeling, vulnerability analyses, and cybersecurity requirements derivation on both system and subsystem levels. Without concrete practice guidelines, the ISO/SAE 21434 is very difficult to understand and apply at this stage. This contribution aims to fill this gap through a model inspired by cutting‐edge embedded cybersecurity practices interpreted for the current and near‐future automotive electronic architectures.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards a security‐driven automotive development lifecycle

Dobaj

Macher

Ekert³

et al. 2021

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although CAN bus can provide reliable communication, it also suffers from some security loopholes. Hackers can use these loopholes to launch attacks to cars, thus affecting the normal driving of them and even endangering the life safety of drivers [ 9 ]. In recent years, cyber attacks on cars have emerged in an endless stream, and there is an increasing trend every year [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A multi-attack intrusion detection model based on Mosaic coded convolutional neural network and centralized encoding

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

With the development of the Internet of Vehicles (IoV), attacks to the vehicle-mounted control area network (CAN) have seriously jeopardized the security of automobiles. As an important security measure, intrusion detection technologies have aroused great interest in researchers and many detection methods have also been proposed based on the vehicle’s CAN bus. However, many studies only considered one type of attack at a time but in real environments there may contain a variety of attack types simultaneously. In view of the deficiency in the current methods, this paper proposed a method to detect multi-intrusions at one time based on a Mosaic coded convolutional neural network (CNN) and a centralized coding method. A Mosaic-like data block was created to convert the one-dimensional CAN ID into a two-dimensional data grid for the CNN to effectively extract the data characteristics and maintain the time characteristics between the CAN IDs. Four types of attacks and all combinations of them were used to train and test our model. Finally, a centralized coding method was used to increase the discrimination capability of the model. Experimental results showed that this single model could successfully detect any combinations of the intrusion types with very high and stable performance.

show abstract

“…To secure vehicular environments, two different approaches have been considered: security countermeasures based on encryption and authentication for data over vehicular networks, and intrusion detection systems that can detect suspicious activities on the networks [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. In this work, we focus on the intrusion detection system based on the estimation of the Rényi entropy with multiple orders [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Multiple Rényi Entropy Based Intrusion Detection System for Connected Vehicles

Kim

Lim

et al. 2020

Entropy

View full text Add to dashboard Cite

In this paper, we propose an intrusion detection system based on the estimation of the Rényi entropy with multiple orders. The Rényi entropy is a generalized notion of entropy that includes the Shannon entropy and the min-entropy as special cases. In 2018, Kim proposed an efficient estimation method for the Rényi entropy with an arbitrary real order α . In this work, we utilize this method to construct a multiple order, Rényi entropy based intrusion detection system (IDS) for vehicular systems with various network connections. The proposed method estimates the Rényi entropies simultaneously with three distinct orders, two, three, and four, based on the controller area network (CAN)-IDs of consecutively generated frames. The collected frames are split into blocks with a fixed number of frames, and the entropies are evaluated based on these blocks. For a more accurate estimation against each type of attack, we also propose a retrospective sliding window method for decision of attacks based on the estimated entropies. For fair comparison, we utilized the CAN-ID attack data set generated by a research team from Korea University. Our results show that the proposed method can show the false negative and positive errors of less than 1% simultaneously.

show abstract

An Automotive Signal-Layer Security and Trust-Boundary Identification Approach

Cited by 10 publications

References 7 publications

Towards a security‐driven automotive development lifecycle

Towards a security‐driven automotive development lifecycle

A multi-attack intrusion detection model based on Mosaic coded convolutional neural network and centralized encoding

A Multiple Rényi Entropy Based Intrusion Detection System for Connected Vehicles

Contact Info

Product

Resources

About