Cyber-manufacturing system (CMS) offers a blueprint for future manufacturing systems in which physical components are fully integrated with computational processes in a connected environment. Similar concepts and visions have been developed to different extents and under different names—“Industrie 4.0” in Germany, “Monozukuri” in Japan, “Factories of the Future” in the EU, and “Industrial Internet” by GE. However, CMS opens a door for cyber–physical attacks on manufacturing systems. Current computer and information security methods—firewalls and intrusion detection system (IDS), etc.—cannot detect the malicious attacks in CMS with adequate response time and accuracy. Realization of the promising CMS depends on addressing cyber–physical security issues effectively. These attacks can cause physical damages to physical components—machines, equipment, parts, assemblies, products—through over-wearing, breakage, scrap parts or other changes that designers did not intend. This research proposes a conceptual design of a system to detect cyber–physical intrusions in CMS. To accomplish this objective, physical data from the manufacturing process level and production system level are integrated with cyber data from network-based and host-based IDSs. The correlations between the cyber and physical data are analyzed. Machine learning methods are adapted to detect the intrusions. Three-dimensional (3D) printing and computer numerical control (CNC) milling process are used as examples of manufacturing processes for detecting cyber–physical attacks. A cyber–physical attack scenario is presented with preliminary results to illustrate how the system can be used.

References

References
1.
Song
,
Z.
, and
Moon
,
Y.
,
2016
, “
Assessing Sustainability Benefits of Cybermanufacturing Systems
,”
Int. J. Adv. Manuf. Technol.
,
90
(
5–8
), pp.
1
18
.
2.
Ren
,
L.
,
Zhang
,
L.
,
Tao
,
F.
,
Zhao
,
C.
,
Chai
,
X.
, and
Zhao
,
X.
,
2015
, “
Cloud Manufacturing: From Concept to Practice
,”
Enterp. Inf. Syst.
,
9
(
2
), pp.
186
209
.
3.
IBM X-Force Research
,
2017
, “Security Trends in the Manufacturing Industry,”
IBM Security
, Cambridge, MA.
4.
Jazdi
,
N.
,
2014
, “
Cyber Physical Systems in the Context of Industry 4.0
,”
IEEE
International Conference on Automation, Quality and Testing, Robotics
, Cluj Napoka, Romania, May 22–24, pp.
2
4
.
5.
Davis
,
J.
,
Edgar
,
T.
,
Porter
,
J.
,
Bernaden
,
J.
, and
Sarli
,
M.
,
2012
, “
Smart Manufacturing, Manufacturing Intelligence and Demand-Dynamic Performance
,”
Comput. Chem. Eng.
,
47
, pp.
145
156
.
6.
Minnick
,
J.
,
2016
, “The Biggest Cybersecurity Problems Facing Manufacturing In 2016,” Manufacturing Business Technology, Madison, WI, accessed Nov. 28, 2018, http://www.mbtmag.com/article/2016/01/biggest-cybersecurity-problems-facing-manufacturing-2016
7.
Pan
,
Y.
,
White
,
J.
,
Schmidt
,
D.
,
Elhabashy
,
A.
,
Sturm
,
L.
,
Camelio
,
J.
, and
Williams, C.
,
2017
, “
Taxonomies for Reasoning About Cyber-Physical Attacks in IoT-Based Manufacturing Systems
,”
Int. J. Interact. Multimed. Artif. Intell.
,
4
(3), p.
45
.
8.
Sturm
,
L. D.
,
Williams
,
C. B.
,
Camelio
,
J. A.
,
White
,
J.
, and
Parker
,
R.
,
2017
, “
Cyber-Physical Vulnerabilities in Additive Manufacturing Systems: A Case Study Attack on the STL File With Human Subjects
,”
J. Manuf. Syst.
,
44
, pp.
154
64
.
9.
Bilge
,
L.
, and
Dumitras
,
T.
,
2012
, “
Before We Knew It: An Empirical Study of Zero-Day Attacks in the Real World
,”
ACM Conference on Computer and Communications Security
(
CCS'12
), Raleigh, NC, Oct. 16–18, pp.
833
44
.
10.
Mitchell
,
R.
, and
Chen
,
I.-R.
,
2014
, “
A Survey of Intrusion Detection Techniques for Cyber-Physical Systems
,”
ACM Comput. Surv.
,
46
(
4
), pp. 55–84.
11.
Liao
,
H.-J.
,
Richard Lin
,
C.-H.
,
Lin
,
Y.-C.
, and
Tung
,
K.-Y.
,
2013
, “
Intrusion Detection System: A Comprehensive Review
,”
J. Network Comput. Appl.
,
36
(
1
), pp.
16
24
.
12.
Debar
,
H.
,
2017
, “
What is Behavior Based Intrusion Detection?
,” SANS Institute, North Bethesda, MD, accessed Nov. 28, 2018, https://www.sans.org/security-resources/
13.
Bitkom
,
V.
,
Vdma
,
V.
, and
Zvei
,
V.
,
2016
, “
Implementation Strategy Industrie 4.0.
,” Berlin, Germany.
14.
Han
,
S.
,
Xie
,
M.
,
Chen
,
H.
, and
Ling
,
Y.
,
2014
, “
Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges
,”
Syst. J.
,
8
(4), pp.
1049
59
.
15.
Wu
,
M.
,
Song
,
Z.
, and
Moon
,
Y. B.
,
2017
, “
Detecting Cyber-Physical Attacks in Cyber Manufacturing Systems With Machine Learning Methods
,”
J. Intell. Manuf.
, (in press).
16.
Langner
,
R.
,
2011
, “
Stuxnet: Dissecting a Cyberwarfare Weapon
,”
IEEE Secur. Privacy
,
9
(
3
), pp.
49
51
.
17.
Lee
,
R. M.
,
Assante
,
M. J.
, and
Conway
,
T.
,
2014
, “
German Steel Mill Cyber Attack
,”
Ind. Control Syst.
, pp.
1
15
.https://ics.sans.org/media/ICS-CPPE-case-Study-2-German-Steelworks_Facility.pdf
18.
Ehrenfeld
,
J. M.
,
2017
, “
WannaCry, Cybersecurity and Health Information Technology: A Time to Act
,”
J. Med. Syst.
,
41
(7), p.
104
.
19.
Kaspersky Lab,
2017
, “
The State of Industrial Cybersecurity 2017
,” Kaspersky, Woburn, MA.
20.
The Seattle Times
,
2018
, “
Boeing Hit by WannaCry Virus, But Says Attack Caused Little Damage
,” The Seattle Times, Seattle, WA, accessed May 20, 2018, https://www.seattletimes.com/business/boeing-aerospace/boeing-hit-by-wannacry-virus-fears-it-could-cripple-some-jet-production/
21.
Sturm
,
L. D.
,
Williams
,
C. B.
,
Camelio
,
J. A.
,
White
,
J.
, and
Parker
,
R.
,
2014
, “
Cyber-Physical Vulnerabilities in Additive Manufacturing Systems
,”
International Solid Freeform Fabrication Symposium
, Storrs, CT, pp.
951
963
.
22.
Turner
,
H.
,
White
,
J.
,
Camelio
,
J. A.
,
Williams
,
C.
,
Amos
,
B.
, and
Parker
,
R.
,
2015
, “
Bad Parts: are Our Manufacturing Systems at Risk of Silent Cyberattacks?
,”
IEEE Secur. Privacy
,
13
(
3
), pp.
40
47
.
23.
Yampolskiy
,
M.
,
Skjellum
,
A.
,
Kretzschmar
,
M.
,
Overfelt
,
R. A.
,
Sloan
,
K. R.
, and
Yasinsac
,
A.
,
2016
, “
Using 3D Printers as Weapons
,”
Int. J. Crit. Infrastruct. Prot.
,
14
, pp.
58
71
.
24.
Belikovetsky
,
S.
,
Yampolskiy
,
M.
,
Toh
,
J.
, and
Elovici
,
Y.
,
2016
, “
Dr0wned—Cyber-Physical Attack With Additive Manufacturing
,” e-print arXiv:1609.00133
25.
Wu
,
M.
, and
Moon
,
Y. B.
,
2017
, “
Taxonomy of Cross-Domain Attacks on Cyber Manufacturing System
,”
Procedia Comput. Sci.
,
114
, pp.
367
374
.
26.
Vincent
,
H.
,
Wells
,
L.
,
Tarazaga
,
P.
, and
Camelio
,
J.
,
2015
, “
Trojan Detection and Side-Channel Analyses for Cyber-Security in Cyber-Physical Manufacturing Systems
,”
Procedia Manuf.
,
1
, pp.
77
85
.
27.
Wu
,
M.
,
Phoha
,
V. V.
,
Moon
,
Y. B.
, and
Belman
,
A. K.
,
2016
, “
Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification
,”
ASME
Paper No. IMECE2016-67641
.
28.
Wu
,
M.
,
Zhou
,
H.
,
Lin
,
L. L.
,
Silva
,
B.
,
Song
,
Z.
,
Cheung
,
J.
, and
Moon
,
Y.
,
2017
, “
Detecting Attacks in Cyber Manufacturing Systems: Additive Manufacturing Example
,”
MATEC Web Conf.
,
108
, p.
06005
.
29.
Wu
,
M.
, and
Moon
,
Y.
,
2018
, “
DACDI (Define, Audit, Correlate, Disclose, and Improve) Framework to Address Cyber-Manufacturing Attacks and Intrusions
,”
Manuf. Lett.
,
15
, pp. 155–159.
30.
Chhetri
,
S. R.
,
Canedo
,
A.
, and
Faruque
,
M. A.
,
2016
, “
KCAD: Kinetic Cyber-Attack Detection Method for Cyber-Physical Additive Manufacturing Systems
,”
35th International Conference on Computer-Aided Design
(
ICCAD '16
), Austin, TX, Nov. 7–10, pp.
1
8
.
31.
Belikovetsky
,
S.
,
Solewicz
,
Y.
,
Yampolskiy
,
M.
,
Toh
,
J.
, and
Elovici
,
Y.
,
2017
, “
Detecting Cyber-Physical Attacks in Additive Manufacturing Using Digital Audio Signing
,” e-print arXiv:1705.06454v1
32.
Adamson
,
G.
,
Wang
,
L.
,
Holm
,
M.
, and
Moore
,
P.
,
2015
, “
Cloud Manufacturing—A Critical Review of Recent Development and Future Trends
,”
Int. J. Comput. Integr. Manuf.
,
30
(4–5), pp. 347–380.
33.
Modi
,
C.
,
Patel
,
D.
,
Patel
,
H.
,
Borisaniya
,
B.
,
Patel
,
A.
, and
Rajarajan
,
M.
,
2013
, “
A Survey of Intrusion Detection Techniques in Cloud
,”
J. Network Comput. Appl.
,
36
(
1
), pp.
42
57
.
34.
Jaeger
,
D.
,
Ussath
,
M.
,
Cheng
,
F.
, and
Meinel
,
C.
,
2016
, “
Multi-Step Attack Pattern Detection on Normalized Event Logs
,”
IEEE
Second International Conference on Cyber Security and Cloud Computing
, New York, Nov. 3–5, pp.
390
398
.
35.
Timofte
,
J.
,
2008
, “
Intrusion Detection Using Open Source Tools
,”
Inform. Econ. J.
,
XII
(
2
), pp.
75
79
.https://core.ac.uk/download/pdf/6612510.pdf
36.
Roesch
,
M.
,
1999
, “
Snort—Lightweight Intrusion Detection for Networks
,”
13th System Administration (LISA '99)
, Seattle, WA, Nov. 7–12, pp.
229
238
.
37.
Kemmerer
,
R. A.
, and
Vigna
,
G.
,
2002
, “
Intrusion Detection: A Brief History and Overview
,”
Computer
,
35
(
4
), pp.
supl27
supl30
.
38.
Shen
,
Q.
,
Gao
,
J.
, and
Li
,
C.
,
2010
, “
Automatic Classification of Weld Defects in Radiographic Images
,”
Insight Non-Destr. Test. Cond. Monit.
,
52
(
3
), pp.
134
139
.
39.
Pernkopf
,
F.
, and
O'Leary
,
P.
,
2003
, “
Image Acquisition Techniques for Automatic Visual Inspection of Metallic Surfaces
,”
NDT E Int.
,
36
(
8
), pp.
609
617
.
40.
Jia
,
H.
,
Murphey
,
Y. L.
,
Shi
,
J.
, and
Chang
,
T. S.
,
2004
, “
An Intelligent Real-Time Vision System for Surface Defect Detection
,” International Conference on Pattern Recognition (
ICPR
), Cambridge, UK, Aug. 26, pp.
239
242
.
41.
Duro
,
J. A.
,
Padget
,
J. A.
,
Bowen
,
C. R.
, and
Kim
,
H. A.
,
2016
, “
Multi-Sensor Data Fusion Framework for CNC Machining Monitoring
,”
Mech. Syst. Signal Process
,
66–67
, pp.
505
520
.
42.
Song
,
C.
,
Lin
,
F.
,
Ba
,
Z.
,
Ren
,
K.
,
Zhou
,
C.
, and
Xu
,
W.
,
2016
, “
My Smartphone Knows What You Print: Exploring Smartphone-Based Side-Channel Attacks Against 3D Printers
,”
ACM SIGSAC Conference on Computer and Communications Security
, Vienna, Austria, Oct. 24–28, pp.
895
907
.
43.
Wu
,
M.
,
Song
,
J.
,
Lin
,
L. W. L.
,
Aurelle
,
N.
,
Liu
,
Y.
,
Ding
,
B.
,
Song
,
Z.
, and
Moon
,
Y. B.
,
2018
, “
Establishment of Intrusion Detection Testbed for Cyber Manufacturing Systems
,”
46th
SME North American Manufacturing Research Conference, College Station, TX, p.
11
.
44.
Sun
,
X.
,
Wang
,
X.
,
Wu
,
J.
, and
Liu
,
Y.
,
2014
, “
Prediction-Based Manufacturing Center Self-Adaptive Demand Side Energy Optimization in Cyber Physical Systems
,”
Chin. J. Mech. Eng.
,
27
(
3
), pp.
488
495
.
45.
Kroll
,
B.
,
Schaffranek
,
D.
,
Schriegel
,
S.
, and
Niggemann
,
O.
,
2014
, “
System Modeling Based on Machine Learning for Anomaly Detection and Predictive Maintenance in Industrial Plants
,” IEEE Emerging Technology and Factory Automation (
ETFA
), Barcelona, Spain, Sept. 16–19, p.
7
.
46.
Alnabulsi
,
H.
,
Islam
,
M. R.
, and
Mamun
,
Q.
,
2014
, “
Detecting SQL Injection Attacks Using SNORT IDS
,”
Asia-Pacific World Congress on Computer Science and Engineering
, Nadi, Fiji, Nov. 4–5.
47.
Patil
,
T. R.
, and
Sherekar
,
S. S.
,
2013
, “
Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification
,”
Int. J. Comput. Sci. Appl.
,
6
(2), pp.
256
261
.http://keddiyan.com/files/AHCI/week2/9.pdf
48.
Kandhari
,
R.
,
Chandola
,
V.
,
Banerjee
,
A.
,
Kumar
,
V.
, and
Kandhari
,
R.
,
2009
, “
Anomaly Detection: A Survey
,”
ACM Comput. Surv.
,
41
(3), pp.
1
6
.
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