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Framework for Identifying Cybersecurity Risks in Manufacturing

Journal Article · · Procedia Manufacturing
 [1];  [2];  [2];  [2];  [3];  [2]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Univ. of California, Berkeley, CA (United States)
  3. Purdue Univ., West Lafayette, IN (United States)
+ Show Author Affiliations

Increasing connectivity, use of digital computation, and off-site data storage provide potential for dramatic improvements in manufacturing productivity, quality, and cost. However, there are also risks associated with the increased volume and pervasiveness of data that are generated and potentially accessible to competitors or adversaries. Enterprises have experienced cyber attacks that exfiltrate confidential and/or proprietary data, alter information to cause an unexpected or unwanted effect, and destroy capital assets. Manufacturers need tools to incorporate these risks into their existing risk management processes. This article establishes a framework that considers the data flows within a manufacturing enterprise and throughout its supply chain. The framework provides several mechanisms for identifying generic and manufacturing-specific vulnerabilities and is illustrated with details pertinent to an automotive manufacturer. Finally, in addition to providing manufacturers with insights into their potential data risks, this framework addresses an outcome identified by the NIST Cybersecurity Framework.

Research Organization:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1340252
Report Number(s):
SAND--2014-19551J; PII: S2351978915010604
Journal Information:
Procedia Manufacturing, Journal Name: Procedia Manufacturing Journal Issue: C Vol. 1; ISSN 2351-9789
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (4)

An intelligent machine monitoring system for energy prediction using a Gaussian Process regression conference October 2014
A Security Framework for Cloud Manufacturing
  • Wang, Yazhe; Ma, Shunan; Ren, Lei
  • ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference, Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing https://doi.org/10.1115/MSEC2014-4082
conference October 2014
Guidelines for Failure Mode and Effects Analysis (FMEA), for Automotive, Aerospace, and General Manufacturing Industries reference-book March 2003
Security and Privacy Controls for Federal Information Systems and Organizations report April 2013

Cited By (9)

High Performance Cutting (HPC) in the New Era of Digital Manufacturing – A Roadmap journal January 2016
Cyber-physical Vulnerability Assessment in Manufacturing Systems journal January 2016
Uzbekistan Towards Industry 4.0. Defining the Gaps Between Current Manufacturing Systems and Industry 4.0 book January 2018
Defending against product-oriented cyber-physical attacks on machining systems journal May 2019
A cyber-physical attack taxonomy for production systems: a quality control perspective journal March 2018
Internet of nuclear things: Managing the proliferation risks of 3-D printing technology journal February 2018
The internet of things for smart manufacturing: A review journal May 2019
Collaborative Cloud Manufacturing: Design of Business Model Innovations Enabled by Cyberphysical Systems in Distributed Manufacturing Systems journal January 2016
A Design Approach to IoT Endpoint Security for Production Machinery Monitoring journal May 2019

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Related Subjects

97 MATHEMATICS AND COMPUTING
Advanced Manufacturing
Cybersecurity
Data Analytics
Risk