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Standard

Cybersecurity Guidebook for Cyber-Physical Vehicle Systems

2016-01-14
CURRENT
J3061_201601
This recommended practice provides guidance on vehicle Cybersecurity and was created based off of, and expanded on from, existing practices which are being implemented or reported in industry, government and conference papers. ...Other proprietary Cybersecurity development processes and standards may have been established to support a specific manufacturer’s development processes, and may not be comprehensively represented in this document, however, information contained in this document may help refine existing in-house processes, methods, etc. ...This recommended practice establishes a set of high-level guiding principles for Cybersecurity as it relates to cyber-physical vehicle systems. This includes: Defining a complete lifecycle process framework that can be tailored and utilized within each organization’s development processes to incorporate Cybersecurity into cyber-physical vehicle systems from concept phase through production, operation, service, and decommissioning.
Journal Article

Cybersecurity Considerations for Heavy Vehicle Event Data Recorders

2018-12-14
Abstract Trust in the digital data from heavy vehicle event data recorders (HVEDRs) is paramount to using the data in legal contests. Ensuring the trust in the HVEDR data requires an examination of the ways the digital information can be attacked, both purposefully and inadvertently. The goal or objective of an attack on HVEDR data will be to have the data omitted in a case. To this end, we developed an attack tree and establish a model for violating the trust needed for HVEDR data. The attack tree provides context for mitigations and also for functional requirements. A trust model is introduced as well as a discussion on what constitutes forensically sound data. The main contribution of this article is an attack tree-based model of both malicious and accidental events contributing to compromised event data recorder (EDR) data. A comprehensive list of mitigations for HVEDR systems results from this analysis.
Magazine

Aerospace & Defense Technology: October 2015

2015-10-01
Countering cybersecurity threats against unmanned vehicle systems Cranfield University researchers have developed a monitoring system whose purpose is to monitor mission profile implementation at both high level mission execution and at lower level software code operation to tackle specific threats of malicious code and possible spurious commands received over a vehicle's data links.
Magazine

SAE Truck & Off-Highway Engineering: October 2018

2018-10-01
Quotes from COMVEC 2018 Industry leaders spoke extensively about all things autonomous-ADAS, big data, connectivity, cybersecurity, machine learning-at the annual SAE event. Here's some of what they had to say. Fuel-cell Class 8-take 2.0 With a longer-range and more-refined fuel cell-powered heavy-duty truck, Toyota aims to eventually eliminate emissions from trucks serving increasingly congested California ports. ...Editorial Bring innovation, disruption in-house Adding 3D printing to design, manufacturing processes Upstream devoted to truck cybersecurity threats Jacobs employs cylinder deactivation in HD engines to lower CO2, NOx Emissions reductions continue to disrupt CV industry Mercedes doubles down on electric vans and buses, considers fuel cells Off-road bus from Torsus transports to hard-to-reach places Q&A Perkins pursues plug-and-play connectivity
Magazine

SAE Truck & Off-Highway Engineering: August 2017

2017-08-03
Connected commercial vehicles bring cybersecurity to the fore Connectivity, automation and electrification will largely drive vehicle developments in the coming years, according to experts presenting at the revamped SAE COMVEC 17.
Technical Paper

Intelligent Vehicle Monitoring for Safety and Security

2019-04-02
2019-01-0129
The caveat to these additional capabilities is issues like cybersecurity, complexity, etc. This paper is an exploration into FuSa and CAVs and will present a systematic approach to understand challenges and propose potential framework, Intelligent Vehicle Monitoring for Safety and Security (IVMSS) to handle faults/malfunctions in CAVs, and specifically autonomous systems.
Standard

SAE J1939 Network Security

2017-03-06
WIP
J1939-91
This document will provide recommendations to vehicle manufacturers and component suppliers in securing the SAE J1939-13 connector interface from the cybersecurity risks posed by the existence of this connector.
Technical Paper

Securing J1939 Communications Using Strong Encryption with FIPS 140-2

2017-03-28
2017-01-0020
Since 2001, all sensitive information of U.S. Federal Agencies has been protected by strong encryption mandated by the Federal Information Processing Standards (FIPS) 140-2 Security Requirements. The requirements specify a formal certification process. The process ensures that validated encryption modules have implemented the standard, and have passed a rigorous testing and review processes. Today, this same strong security protection has become possible for vehicle networks using modern, cost-effective encryption in hardware. This paper introduces the motivation and context for the encryption diagnostics security in terms of all vehicles in general, not just trucks which use SAE J1939 communications. Several practical scenarios for using such encryption hardware and the advantages of using hardware compared to software private-key encryption and public-key encryption are described.
Research Report

Unsettled Topics Concerning Sensors for Automated Road Vehicles

2018-10-18
EPR2018001
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the new generation of sensors designed for vehicles capable of automated driving. Four main issues are outlined that merit immediate interest: First, specifying a standardized terminology and taxonomy to be used for discussing the sensors required by automated vehicles. Second, generating standardized tests and procedures for verifying, simulating, and calibrating automated driving sensors. Third, creating a standardized set of tools and methods to ensure the security, robustness, and integrity of data collected by such sensors. The fourth issue, regarding the ownership and privacy of data collected by automated vehicle sensors, is considered only briefly here since its scope far exceeds the technical issues that are the primary focus of the present report. SAE EDGE™ Research Reports are preliminary investigations of new technologies.
Magazine

SAE Truck & Off-Highway Engineering: February 2019

2019-02-01
Over-the-air affair Remote updating of software and firmware on commercial trucks and off-highway machines is on the rise, not only for maintenance functions but also to add new features like operator-assist technology. Developments in engine-based gensets With demand for generator sets steady and regulatory change settling, suppliers can rationalize their offerings and push improvements in areas like noise abatement and economy. Testing, testing and even more testing The commercial-vehicle market is eager to adopt more ADAS and automated-driving innovations, but before those technologies get to the road they must first pass rigorous testing practices that prove their efficacy. Smart and connected powertrains FPT Tech Day reveals multi-power Cursor X concept, other "4.0 innovations" for hydrogen fuel cell, electric and natural gas propulsion.
Technical Paper

Recognizing Manipulated Electronic Control Units

2015-04-14
2015-01-0202
Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.
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