Abstract

The advancement of sensing technology enables efficient data collection from manufacturing systems for monitoring and control. Furthermore, with the rapid development of the Internet of Things (IoT) and information technologies, more and more manufacturing systems become cyber-enabled, facilitating real-time data sharing and information exchange, which significantly improves the flexibility and efficiency of manufacturing systems. However, the cyber-enabled environment may pose the collected sensor data with high risks of cyber-physical attacks during the data and information sharing. Specifically, cyber-physical attacks could target the manufacturing process and/or the data transmission process to maliciously tamper the sensor data, resulting in false alarms or failures in anomaly detection in monitoring. In addition, cyber-physical attacks may also enable illegal data access without authorization and cause the leakage of key product/process information. Therefore, it becomes critical to develop an effective approach to protect data from these attacks so that the cyber-physical security of the manufacturing systems can be assured in the cyber-enabled environment. To achieve this goal, this paper proposes an integrative blockchain-enabled data protection method by leveraging camouflaged asymmetry encryption. A real-world case study that protects the cyber-physical security of collected sensor data in additive manufacturing is presented to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time (less than 0.05 ms), and the risk of unauthorized data access is significantly reduced as well.

References

1.
Yang
,
H.
,
Kumara
,
S.
,
Bukkapatnam
,
S. T.
, and
Tsung
,
F.
,
2019
, “
The Internet of Things for Smart Manufacturing: A Review
,”
IISE Trans.
,
51
(
11
), pp.
1190
1216
.
2.
Chaduvula
,
S. C.
,
Dachowicz
,
A.
,
Atallah
,
M. J.
, and
Panchal
,
J. H.
,
2018
, “
Security in Cyber-Enabled Design and Manufacturing: A Survey
,”
ASME J. Comput. Inf. Sci. Eng.
,
18
(
4
), p.
040802
.
3.
DeSmit
,
Z.
,
Elhabashy
,
A. E.
,
Wells
,
L. J.
, and
Camelio
,
J. A.
,
2017
, “
An Approach to Cyber-Physical Vulnerability Assessment for Intelligent Manufacturing Systems
,”
J. Manuf. Syst.
,
43
, pp.
339
351
.
4.
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
164
.
5.
Brandman
,
J.
,
Sturm
,
L.
,
White
,
J.
, and
Williams
,
C.
,
2020
, “
A Physical Hash for Preventing and Detecting Cyber-Physical Attacks in Additive Manufacturing Systems
,”
J. Manuf. Syst.
,
56
, pp.
202
212
.
6.
Liu
,
C.
,
Kan
,
C.
, and
Tian
,
W.
,
2020
, “
An Online Side Channel Monitoring Approach for Cyber-Physical Attack Detection of Additive Manufacturing
,”
International Manufacturing Science and Engineering Conference
,
Virtual, Online
,
Sept. 3
.
7.
Zheng
,
Z.
,
Xie
,
S.
,
Dai
,
H.
,
Chen
,
X.
, and
Wang
,
H.
,
2017
, “
An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends
,”
2017 IEEE International Congress on big Data (BigData Congress)
,
Honolulu, HI
,
June 25–30
.
8.
Bokhari
,
M. U.
, and
Shallal
,
Q. M.
,
2016
, “
A Review on Symmetric Key Encryption Techniques in Cryptography
,”
Int. J. Comput. Appl. Technol.
,
147
(
10
), pp.
43
48
.
9.
Conti
,
M.
,
Dragoni
,
N.
, and
Lesyk
,
V.
,
2016
, “
A Survey of Man in the Middle Attacks
,”
IEEE Commun. Surv. Tutor.
,
18
(
3
), pp.
2027
2051
.
10.
Shi
,
Z.
,
Kan
,
C.
,
Tian
,
W.
, and
Liu
,
C.
,
2021
, “
A Blockchain-Based G-Code Protection Approach for Cyber-Physical Security in Additive Manufacturing
,”
ASME J. Comput. Inf. Sci. Eng.
,
21
(
4
), p.
041007
.
11.
Zeltmann
,
S. E.
,
Gupta
,
N.
,
Tsoutsos
,
N. G.
,
Maniatakos
,
M.
,
Rajendran
,
J.
, and
Karri
,
R.
,
2016
, “
Manufacturing and Security Challenges in 3D Printing
,”
J. Oper. Manage.
,
68
(
7
), pp.
1872
1881
.
12.
Rokka Chhetri
,
S.
, and
Al Faruque
,
M. A.
,
2017
, “
Side Channels of Cyber-Physical Systems: Case Study in Additive Manufacturing
,”
IEEE Des. Test
,
34
(
4
), pp.
18
25
.
13.
Villalobos
,
K.
,
Suykens
,
J.
, and
Illarramendi
,
A.
,
2021
, “
A Flexible Alarm Prediction System for Smart Manufacturing Scenarios Following a Forecaster–Analyzer Approach
,”
J. Intell. Manuf.
,
32
(
5
), pp.
1323
1344
.
14.
Wu
,
M.
,
Song
,
Z.
, and
Moon
,
Y. B.
,
2019
, “
Detecting Cyber-Physical Attacks in CyberManufacturing Systems With Machine Learning Methods
,”
J. Intell. Manuf.
,
30
(
3
), pp.
1111
1123
.
15.
Shi
,
Z.
,
Mamun
,
A. A.
,
Kan
,
C.
,
Tian
,
W.
, and
Liu
,
C.
,
2022
, “
An LSTM-Autoencoder Based Online Side Channel Monitoring Approach for Cyber-Physical Attack Detection in Additive Manufacturing
,”
J. Intell. Manuf.
,
34
(
4
), pp.
1815
1831
.
16.
Liu
,
C.
,
Kong
,
Z.
,
Babu
,
S.
,
Joslin
,
C.
, and
Ferguson
,
J.
,
2021
, “
An Integrated Manifold Learning Approach for High-Dimensional Data Feature Extractions and Its Applications to Online Process Monitoring of Additive Manufacturing
,”
IISE Trans.
,
53
(
11
), pp.
1215
1230
.
17.
Liu
,
C.
,
Law
,
A. C. C.
,
Roberson
,
D.
, and
Kong
,
Z. J.
,
2019
, “
Image Analysis-Based Closed Loop Quality Control for Additive Manufacturing With Fused Filament Fabrication
,”
J. Manuf. Syst.
,
51
, pp.
75
86
.
18.
Dastoorian
,
R.
, and
Wells
,
L. J.
,
2021
, “
A Hybrid Off-Line/On-Line Quality Control Approach for Real-Time Monitoring of High-Density Datasets
,”
J. Intell. Manuf.
,
34
(
2
), pp.
669
682
.
19.
Larsen
,
S.
, and
Hooper
,
P. A.
,
2021
, “
Deep Semi-Supervised Learning of Dynamics for Anomaly Detection in Laser Powder Bed Fusion
,”
J. Intell. Manuf.
,
33
(
2
), pp.
457
471
.
20.
Ye
,
Z.
,
Liu
,
C.
,
Tian
,
W.
, and
Kan
,
C.
,
2021
, “
In-Situ Point Cloud Fusion for Layer-Wise Monitoring of Additive Manufacturing
,”
J. Manuf. Syst.
,
61
, pp.
210
222
.
21.
Al Mamun
,
A.
,
Liu
,
C.
,
Kan
,
C.
, and
Tian
,
W.
,
2022
, “
Securing Cyber-Physical Additive Manufacturing Systems by In-Situ Process Authentication Using Streamline Video Analysis
,”
J. Manuf. Syst.
,
62
, pp.
429
440
.
22.
Liu
,
C.
,
Tian
,
W.
, and
Kan
,
C.
,
2022
, “
When AI Meets Additive Manufacturing: Challenges and Emerging Opportunities for Human-Centered Products Development
,”
J. Manuf. Syst.
,
64
, pp.
648
656
.
23.
Li
,
Y.
,
Shi
,
Z.
, and
Liu
,
C.
,
2023
, “
Transformer-Enabled Generative Adversarial Imputation Network With Selective Generation (SGT-GAIN) for Missing Region Imputation
,”
IISE Trans.
24.
Li
,
R.
,
Jin
,
M.
, and
Paquit
,
V. C.
,
2021
, “
Geometrical Defect Detection for Additive Manufacturing With Machine Learning Models
,”
Mater. Des.
,
206
, p.
109726
.
25.
Elhabashy
,
A. E.
,
Wells
,
L. J.
, and
Camelio
,
J. A.
,
2020
, “
Cyber-Physical Attack Vulnerabilities in Manufacturing Quality Control Tools
,”
Qual. Eng.
,
32
(
4
), pp.
676
692
.
26.
Elhabashy
,
A. E.
,
Wells
,
L. J.
,
Camelio
,
J. A.
, and
Woodall
,
W. H.
,
2019
, “
A Cyber-Physical Attack Taxonomy for Production Systems: A Quality Control Perspective
,”
J. Intell. Manuf.
,
30
(
6
), pp.
2489
2504
.
27.
Flank
,
S.
,
Nassar
,
A. R.
,
Simpson
,
T. W.
,
Valentine
,
N.
, and
Elburn
,
E.
,
2017
, “
Fast Authentication of Metal Additive Manufacturing
,”
3D Print. Addit. Manuf.
,
4
(
3
), pp.
143
148
.
28.
Komolafe
,
T.
,
Tian
,
W.
,
Purdy
,
G. T.
,
Albakri
,
M.
,
Tarazaga
,
P.
, and
Camelio
,
J.
,
2019
, “
Repeatable Part Authentication Using Impedance Based Analysis for Side-Channel Monitoring
,”
J. Manuf. Syst.
,
51
, pp.
42
51
.
29.
Wu
,
D.
,
Rosen
,
D. W.
,
Wang
,
L.
, and
Schaefer
,
D.
,
2015
, “
Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation
,”
Comput.-Aided Des.
,
59
, pp.
1
14
.
30.
Yen
,
I.-L.
,
Zhang
,
S.
,
Bastani
,
F.
, and
Zhang
,
Y.
,
2017
, “
A Framework for IoT-Based Monitoring and Diagnosis of Manufacturing Systems
,”
2017 IEEE Symposium on Service-Oriented System Engineering (SOSE)
,
San Francisco, CA
,
Apr. 6–9
.
31.
Saeed
,
A.
,
Ahmadinia
,
A.
,
Javed
,
A.
, and
Larijani
,
H.
,
2016
, “
Random Neural Network Based Intelligent Intrusion Detection for Wireless Sensor Networks
,”
Procedia Comput. Sci.
,
80
, pp.
2372
2376
.
32.
Zhang
,
Y.
,
Xu
,
X.
,
Liu
,
A.
,
Lu
,
Q.
,
Xu
,
L.
, and
Tao
,
F.
,
2019
, “
Blockchain-Based Trust Mechanism for IoT-Based Smart Manufacturing System
,”
IEEE Trans. Comput. Soc. Syst.
,
6
(
6
), pp.
1386
1394
.
33.
Kurpjuweit
,
S.
,
Schmidt
,
C. G.
,
Klöckner
,
M.
, and
Wagner
,
S. M.
,
2021
, “
Blockchain in Additive Manufacturing and Its Impact on Supply Chains
,”
J. Bus. Logist.
,
42
(
1
), pp.
46
70
.
34.
Aitzhan
,
N. Z.
, and
Svetinovic
,
D.
,
2016
, “
Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams
,”
IEEE Trans. Dependable Secure Comput.
,
15
(
5
), pp.
840
852
.
35.
Javaid
,
U.
, and
Sikdar
,
B.
,
2021
, “
A Checkpoint Enabled Scalable Blockchain Architecture for Industrial Internet of Things
,”
IEEE Trans. Industr. Inform.
,
17
(
11
), pp.
7679
7687
.
36.
Ghuli
,
P.
,
Kumar
,
U. P.
, and
Shettar
,
R.
,
2017
, “
A Review on Blockchain Application for Decentralized Decision of Ownership of IoT Devices
,”
Adv. Comput. Sci. Technol.
,
10
(
8
), pp.
2449
2456
.
37.
Bahga
,
A.
, and
Madisetti
,
V. K.
,
2016
, “
Blockchain Platform for Industrial Internet of Things
,”
J. Softw. Eng. Appl.
,
9
(
10
), pp.
533
546
.
38.
Yu
,
C.
,
Zhang
,
L.
,
Zhao
,
W.
, and
Zhang
,
S.
,
2020
, “
A Blockchain-Based Service Composition Architecture in Cloud Manufacturing
,”
Int. J. Comput. Integr. Manuf.
,
33
(
7
), pp.
701
715
.
39.
Shafagh
,
H.
,
Burkhalter
,
L.
,
Hithnawi
,
A.
, and
Duquennoy
,
S.
,
2017
, “
Towards Blockchain-Based Auditable Storage and Sharing of IoT Data
,”
Proceedings of the 2017 on Cloud Computing Security Workshop
,
Dallas, TX
,
Nov. 3
.
40.
Schleich
,
B.
,
Anwer
,
N.
,
Mathieu
,
L.
, and
Wartzack
,
S.
,
2017
, “
Shaping the Digital Twin for Design and Production Engineering
,”
CIRP Ann.
,
66
(
1
), pp.
141
144
.
41.
Kennedy
,
Z. C.
,
Stephenson
,
D. E.
,
Christ
,
J. F.
,
Pope
,
T. R.
,
Arey
,
B. W.
,
Barrett
,
C. A.
, and
Warner
,
M. G.
,
2017
, “
Enhanced Anti-Counterfeiting Measures for Additive Manufacturing: Coupling Lanthanide Nanomaterial Chemical Signatures With Blockchain Technology
,”
J. Mater. Chem. C
,
5
(
37
), pp.
9570
9578
.
42.
Peterson
,
K.
,
Deeduvanu
,
R.
,
Kanjamala
,
P.
, and
Boles
,
K.
,
2016
, “
A Blockchain-Based Approach to Health Information Exchange Networks
,”
Use of Blockchain in Healthcare and Research Workshop
,
Gaithersburg, MD
,
Sept. 26–27
.
43.
Merkle
,
R. C.
,
1989
, “
One Way Hash Functions and DES
,”
Conference on the Theory and Application of Cryptology
,
Santa Barbara, CA
,
August
.
44.
Dasgupta
,
D.
,
Shrein
,
J. M.
, and
Gupta
,
K. D.
,
2019
, “
A Survey of Blockchain From Security Perspective
,”
J. Bank. Financ. Technol.
,
3
(
1
), pp.
1
17
.
45.
Gaubatz
,
G.
,
Kaps
,
J.-P.
, and
Sunar
,
B.
,
2004
, “
Public Key Cryptography in Sensor Networks—Revisited
,”
European Workshop on Security in Ad-Hoc and Sensor Networks
,
Heidelberg, Germany
,
Aug. 6
.
46.
Kerry
,
C. F.
, and
Gallagher
,
P. D.
2013
, “
Digital Signature Standard (DSS)
,” FIPS 186-4.
47.
Rivest
,
R. L.
,
Shamir
,
A.
, and
Adleman
,
L.
,
1978
, “
A Method for Obtaining Digital Signatures and Public-Key Cryptosystems
,”
Commun. ACM
,
21
(
2
), pp.
120
126
.
48.
Koblitz
,
N.
,
Menezes
,
A.
, and
Vanstone
,
S.
,
2000
, “
The State of Elliptic Curve Cryptography
,”
Des. Codes, Cryptogr.
,
19
(
2/3
), pp.
173
193
.
49.
Mahto
,
D.
, and
Yadav
,
D. K.
,
2018
, “
Performance Analysis of RSA and Elliptic Curve Cryptography
,”
Int. J. Netw. Secur.
,
20
(
4
), pp.
625
635
.
50.
Saho
,
N. J. G.
, and
Ezin
,
E. C.
,
2020
, “
Comparative Study on the Performance of Elliptic Curve Cryptography Algorithms With Cryptography Through RSA Algorithm
,”
CARI 2020-Colloque Africain sur la Recherche en Informatique et en Mathématiques Apliquées
,
Senegal
,
October
.
51.
Cheng
,
C.
,
Lu
,
R.
,
Petzoldt
,
A.
, and
Takagi
,
T.
,
2017
, “
Securing the Internet of Things in a Quantum World
,”
IEEE Commun. Mag.
,
55
(
2
), pp.
116
120
.
52.
Collberg
,
C.
,
Thomborson
,
C.
, and
Low
,
D.
,
1997
,
A Taxonomy of Obfuscating Transformations
,
Department of Computer Science, The University of Auckland
,
Auckland, New Zealand
.
53.
Bakken
,
D. E.
,
Rarameswaran
,
R.
,
Blough
,
D. M.
,
Franz
,
A. A.
, and
Palmer
,
T. J.
,
2004
, “
Data Obfuscation: Anonymity and Desensitization of Usable Data Sets
,”
IEEE Secur. Priv.
,
2
(
6
), pp.
34
41
.
54.
Gomatam
,
S.
,
Karr
,
A.
, and
Sanil
,
A.
,
2005
, “
Data Swapping as a Decision Problem
,”
J. Off. Stat.
,
21
(
4
), pp.
635
655
.
55.
Boneh
,
D.
, and
Shacham
,
H.
,
2002
, “
Fast Variants of RSA
,”
CryptoBytes
,
5
(
1
), pp.
1
9
.
You do not currently have access to this content.