Abstract

To enable Industry 4.0 successfully, there is a need to build a resilient automation system that can quickly recover after having been attacked or robustly sustain continued operations while being threatened, enable an automated monitoring evolution via various sensor channels in real time, and use advanced machine learning and data analytics to formulate strategies to mitigate and eliminate faults, threats, and malicious attacks. It is envisioned that if we can develop an intelligent model that (a) represents a meaningful, realistic environment and complex entity containing manufacturing Internet of Things interdependent and independent properties that are stepping-stones of the cyber kill chain or precursors of the onset of cyberattacks; (b) can learn and predict potential errors and formulate offense/defense strategies and healing solutions; (c) can enable cognitive ability and human-in-the-loop analytics in real time; and (d) can facilitate system behavior changes to disrupt the attack cascade, then the hosting system can learn how to neutralize threats and attacks and self-repair infected or damaged links autonomously. In this article, our preliminary work presents a visual analytics framework and technique for situational awareness, including autonomously monitoring, diagnosing, and prognosticating the state of cyber-physical systems. Our approach, presented in this article, relies on visual characterizations of multivariate time series and real-time predictive analytics to highlight potential faults, threats, and malicious attacks. To validate the usefulness of our approach, we demonstrate the developed technique using various aviation datasets obtained from the Prognostics Center of Excellence at the National Aeronautics and Space Administration Ames.

References

1.
2.
Department of Energy “DOE Announces $70 Million for Cybersecurity Institute for Energy Efficient Manufacturing,” Department of Energy,
2019
. http://web.archive.org/web/20191030035111/https://www.energy.gov/articles/doe-announces-70-millioncybersecurity-institute-energy-efficient-manufacturing
3.
Chhetri
S. R.
,
Faezi
S.
,
Rashid
N.
, and
Al Faruque
M. A.
, “
Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0
,”
Journal of Hardware and Systems Security
2
, no. 
1
(March
2018
):
51
68
. https://doi.org/10.1007/s41635-017-0031-0
4.
Mansfield
S.
-Devine, “
Securing Small and Medium-Size Businesses
,”
Network Security
2016
, no. 
7
(July
2016
):
14
20
. https://doi.org/10.1016/S1353-4858(16)30070-8
5.
Osborn
E.
,
Business versus Technology: Sources of the Perceived Lack of Cyber Security in SMEs, CDT Technical Paper 01/15
(
Oxford, UK
:
University of Oxford
,
2014
).
6.
Wang
Y.
,
Anokhin
O.
, and
Anderl
R.
, “
Concept and Use Case Driven Approach for Mapping IT Security Requirements on System Assets and Processes in Industrie 4.0
,”
Procedia CIRP
63
(
2017
):
207
212
. https://doi.org/10.1016/j.procir.2017.03.142
7.
Tuptuk
N.
and
Hailes
S.
, “
Security of Smart Manufacturing Systems
,”
Journal of Manufacturing Systems
47
(April
2018
):
93
106
. https://doi.org/10.1016/j.jmsy.2018.04.007
8.
Mahoney
T. C.
and
Davis
J.
,
Cybersecurity for Manufacturers: Securing the Digitized and Connected Factory, Report Number MF-TR-2017-0202
(
Ann Arbor, MI
:
MForesight
,
2017
).
9.
Mehnen
J.
,
He
H.
,
Tedeschi
S.
, and
Tapoglou
N.
, “
Practical Security Aspects of the Internet of Things
,” in
Cybersecurity for Industry 4.0
(
Cham, Switzerland
:
Springer
,
2017
),
225
242
.
10.
Spyridopoulos
T.
,
Tryfonas
T.
, and
May
J.
, “
Incident Analysis & Digital Forensics in SCADA and Industrial Control Systems
,” in
Eighth IET International System Safety Conference Incorporating the Cyber Security Conference
(
Stevenage, UK
:
Institution of Engineering and Technology
,
2013
),
1
6
.
11.
Thames
L.
and
Schaefer
D.
, “
Cybersecurity for Industry 4.0 and Advanced Manufacturing Environments with Ensemble Intelligence
,” in
Cybersecurity for Industry 4.0
(
Cham, Switzerland
:
Springer
,
2017
),
243
265
.
12.
Glavach
D.
,
LaSalle-DeSantis
J.
, and
Zimmerman
S.
, “
Applying and Assessing Cybersecurity Controls for Direct Digital Manufacturing (DDM) Systems
,” in
Cybersecurity for Industry 4.0
(
Cham, Switzerland
:
Springer
,
2017
),
173
194
.
13.
Dang
T. N.
,
Anand
A.
, and
Wilkinson
L.
, “
Timeseer: Scagnostics for High-Dimensional Time Series
,”
IEEE Transactions on Visualization and Computer Graphics
19
, no. 
3
(March
2013
):
470
483
. https://doi.org/10.1109/TVCG.2012.128
14.
Derguech
W.
,
Bruke
E.
, and
Curry
E.
, “
An Autonomic Approach to Real-Time Predictive Analytics Using Open Data and Internet of Things
,” in
2014 IEEE 11th International Conference on Ubiquitous Intelligence and Computing and 2014 IEEE 11th International Conference on Autonomic and Trusted Computing and 2014 IEEE 14th International Conference on Scalable Computing and Communications and Its Associated Workshops
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2014
),
204
211
.
15.
Saxena
A.
and
Goebel
K.
,
Turbofan Engine Degradation Simulation Data Set
(
Moffett Field, CA
:
NASA Ames Research Center
,
2008
).
16.
National Institute of Standards and Technology “
Cybersecurity Framework
,” National Institute of Standards and Technology,
2019
. http://web.archive.org/web/20191030004314/https://www.nist.gov/cyberframework
17.
Andrienko
N.
,
Lammarsch
T.
,
Andrienko
G.
,
Fuchs
G.
,
Keim
D.
,
Miksch
S.
, and
Rind
A.
, “
Viewing Visual Analytics as Model Building
,”
Computer Graphics Forum
37
, no. 
6
(September
2018
):
275
299
. https://doi.org/10.1111/cgf.13324
18.
Xin
D.
,
Ma
L.
,
Liu
J.
,
Macke
S.
,
Song
S.
, and
Parameswaran
A.
, “Accelerating Human-in-the-Loop Machine Learning: Challenges and Opportunities,” in
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning
(
New York
:
Association for Computing Machinery
,
2018
).
19.
Nielsen
M. A.
,
Neural Networks and Deep Learning
(
San Francisco, CA
:
Determination Press
,
2015
).
20.
Ceneda
D.
,
Gschwandtner
T.
,
May
T.
,
Miksch
S.
,
Schulz
H.-J.
,
Streit
M.
, and
Tominski
C.
, “
Characterizing Guidance in Visual Analytics
,”
IEEE Transactions on Visualization and Computer Graphics
23
, no. 
1
(January
2016
):
111
120
. https://doi.org/10.1109/TVCG.2016.2598468
21.
Collins
C.
,
Andrienko
N.
,
Schreck
T.
,
Yang
J.
,
Choo
J.
,
Engelke
U.
,
Jena
A.
, and
Dwyer
T.
, “
Guidance in the Human–Machine Analytics Process
,”
Visual Informatics
2
, no. 
3
(September
2018
):
166
180
. https://doi.org/10.1016/j.visinf.2018.09.003
22.
Dou
W.
,
Jeong
D. H.
,
Stukes
F.
,
Ribarsky
W.
,
Lipford
H. R.
, and
Chang
R.
, “
Recovering Reasoning Processes from User Interactions
,”
IEEE Computer Graphics and Applications
29
, no. 
3
(May–June
2009
):
52
61
. https://doi.org/10.1109/MCG.2009.49
23.
Brown
E. T.
,
Liu
J.
,
Brodley
C. E.
, and
Chang
R.
, “
Dis-function: Learning Distance Functions Interactively
,” in
IEEE Symposium on Visual Analytics Science and Technology
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2012
),
83
92
.
24.
Keim
D. A.
, “
Information Visualization and Visual Data Mining
,”
IEEE Transactions on Visualization and Computer Graphics
8
, no. 
1
(January–March
2002
):
1
8
. https://doi.org/10.1109/2945.981847
25.
Kennedy
J. D.
and
Towhidnejad
M.
, “
Innovation and Certification in Aviation Software
,” in
Integrated Communications, Navigation and Surveillance Conference
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2017
), 3D3-1–3D3-15.
26.
Abeyratne
R.
,
Strategic Issues in Air Transport: Legal, Economic and Technical Aspects
(
Berlin, Germany
:
Springer
,
2012
).
27.
Rush
J.
, “
Super Puma that Crashed into the North Sea Killing 16 Men Was Declared Fit for Service the Day before the Tragedy, an Inquiry Has Heard
,” Daily Mail,
2014
. http://web.archive.org/web/20191030004934/https://www.dailymail.co.uk/news/article-2535958/Super-Puma-crashed-North-Sea-killing-16-men-declared-fit-service-day-tragedy-inquiry-heard.html
29.
Bostock
M.
,
Ogievetsky
V.
, and
Heer
J.
, “
D3 Data-Driven Documents
,”
IEEE Transactions on Visualization and Computer Graphics
17
, no. 
12
(December
2011
):
2301
2309
. https://doi.org/10.1109/TVCG.2011.185
30.
Abadi
M.
,
Barham
P.
,
Chen
J.
,
Chen
Z.
,
Davis
A.
,
Dean
J.
,
Devin
M.
, et al., “
Tensorflow: A System for Large-Scale Machine Learning
,” in
12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)
(
Berkeley, CA
:
USENIX
,
2016
),
265
283
.
31.
Dang
T.
, “
Visualizing Multidimensional Health Status of Data Centers
,” in
Programming and Performance Visualization Tools
(
Cham, Switzerland
:
Springer
,
2017
),
273
283
.
32.
Wilkinson
L.
, “
Visualizing Big Data Outliers through Distributed Aggregation
,”
IEEE Transactions on Visualization and Computer Graphics
24
, no. 
1
(January
2018
):
256
266
. https://doi.org/10.1109/TVCG.2017.2744685
33.
Dang
T. N.
and
Wilkinson
L.
, “
TimeExplorer: Similarity Search Time Series by Their Signatures
,” in
International Symposium on Visual Computing
(
Berlin, Germany
:
Springer
,
2013
),
280
289
.
34.
Wilkinson
L.
,
Anand
A.
, and
Grossman
R.
, “Graph-Theoretic Scagnostics,” in
IEEE Symposium on Information Visualization
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2005
),
157
164
.
35.
Dang
T. N.
and
Wilkinson
L.
, “
Transforming Scagnostics to Reveal Hidden Features
,”
IEEE Transactions on Visualization and Computer Graphics
20
, no. 
12
(December
2014
):
1624
1632
. https://doi.org/10.1109/TVCG.2014.2346572
36.
Dang
T. N.
and
Wilkinson
L.
, “
ScagExplorer: Exploring Scatterplots by Their Scagnostics
,” in
2014 IEEE Pacific Visualization Symposium
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2014
),
73
80
.
37.
Holten
D.
, “
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data
,”
IEEE Transactions on Visualization and Computer Graphics
12
, no. 
5
(September–October
2006
):
741
748
. https://doi.org/10.1109/TVCG.2006.147
38.
Amar
R.
,
Eagan
J.
, and
Stasko
J.
, “
Low-Level Components of Analytic Activity in Information Visualization
,” in
IEEE Symposium on Information Visualization
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2005
),
111
117
.
39.
Shneiderman
B.
, “
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
,” in
IEEE Symposium on Visual Languages
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
1996
),
336
343
.
40.
Borgo
R.
,
Kehrer
J.
,
Chung
D. H. S.
,
Maguire
E.
,
Laramee
R. S.
,
Hauser
H.
,
Ward
M.
, and
Chen
M.
, “
Glyph-Based Visualization: Foundations, Design Guidelines, Techniques and Applications
,” in
Eurographics
(
Geneva, Switzerland
:
The Eurographics Association
,
2013
),
39
63
.
41.
Cappers
B. C. M.
,
Meessen
P. N.
,
Etalle
S.
, and
van Wijk
J. J.
, “
Eventpad: Rapid Malware Analysis and Reverse Engineering Using Visual Analytics
,” in
IEEE Symposium on Visualization for Cyber Security (VizSec)
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2018
),
1
8
.
42.
Frederick
D. K.
,
DeCastro
J. A.
, and
Litt
J. S.
,
User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), NASA/TM—2007-215026
(
Washington, DC
:
National Aeronautics and Space Administration
,
2007
).
43.
Ramasso
E.
and
Saxena
A.
, “
Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets
,”
International Journal of Prognostics and Health Management
5
(
2014
):
1
15
.
44.
Wang
T.
,
Yu
J.
,
Siegel
D.
, and
Lee
J.
, “
A Similarity-Based Prognostics Approach for Remaining Useful Life Estimation of Engineered Systems
,” in
International Conference on Prognostics and Health Management
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2008
),
1
6
.
45.
Heimes
F. O.
, “
Recurrent Neural Networks for Remaining Useful Life Estimation
,” in
International Conference on Prognostics and Health Management
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2008
),
1
6
.
46.
Peel
L.
, “
Data Driven Prognostics Using a Kalman Filter Ensemble of Neural Network Models
,” in
International Conference on Prognostics and Health Management
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2008
),
1
6
.
47.
Hochreiter
S.
and
Schmidhuber
J.
, “
Long Short-Term Memory
,”
Neural Computation
9
, no. 
8
(
1997
):
1735
1780
. https://doi.org/10.1162/neco.1997.9.8.1735
48.
LeCun
Y.
,
Bengio
Y.
, and
Hinton
G.
, “
Deep Learning
,”
Nature
521
, no. 
7553
(May
2015
):
436
444
. https://doi.org/10.1038/nature14539
49.
Zheng
S.
,
Ristovski
K.
,
Farahat
A.
, and
Gupta
C.
, “
Long Short-Term Memory Network for Remaining Useful Life Estimation
,” in
IEEE International Conference on Prognostics and Health Management
(
Piscataway, NJ
:
Institute of Electrical and Electronics Engineers
,
2017
),
88
95
.
50.
Jayasinghe
L.
,
Samarasinghe
T.
,
Yuen
C.
,
Low
J. C. N.
, and
Ge
S. S.
, “
Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery
,”
arXiv preprint arXiv:1810.05644
,
2018
.
51.
Interactive Data Visualization Lab “Explainable Neural Networks for Multivariate Time Series,”
2019
. http://web.archive.org/web/20191030054904/https://idatavisualizationlab.github.io/V/RUL_Viz/
52.
Goodfellow
I. J.
,
Shlens
J.
, and
Szegedy
C.
, “
Explaining and Harnessing Adversarial Examples
,”
arXiv preprint arXiv:1412.6572
,
2014
.
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