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TRANSCRIPT
Yi Qian
Department of Electrical and Computer EngineeringUniversity of Nebraska, NE, USA
E-mail: [email protected]
Web: cns.unl.edu/yqian
October 18, 2016
Recent Research in Smart Recent Research in Smart Recent Research in Smart Recent Research in Smart Grid Communication Grid Communication Grid Communication Grid Communication
Infrastructures and Cyber SecurityInfrastructures and Cyber SecurityInfrastructures and Cyber SecurityInfrastructures and Cyber Security
Introduction to Smart Grid
2
SMARTGRID
UPGADE
CO
NT
RO
LDEMANDRESPONSE
GENERATORRENEWABLE
SO
UR
CE
S
EFF
ICIE
NC
YPEAK
LOAD
AD
VA
NC
ED
COMMUNICATIONS
WIRELESSTRANSMISSION
RELIABILITYENHANCED
EVOLUTION
ELECTRICITY
SY
ST
EM
DE
VIC
ES
INFORMATION
NETWORK
TECHNOLOGIES
BIG
DA
TA
CLOUD
COMPUTING
MET
ER
SMOOTH
CONSUMERSERVICE
TWO-WAY
Motivations & Objectives of Smart Grid
3
• Higher Penetration
of Renewables
• Smart Charging of
Electric Vehicles
• Consumers to
Control Energy Bills
• Efficient Grid
Operations &
Reduced Losses
• Reduced
Distribution
Outages
• Improved System
Reliability &
Security
Lower Greenhouse Gas Emission
Facilitated renewable resource generation
Increased productivity
Improved utilization
Enhanced customer experience
Adherence to regulatory constraints
What is Smart Grid?
• An upgrade on upgrading generation, transmission, and distribution systems
• Incorporating advanced information and communications technologies (ICT) and control
• DoE defines Smart Grid in terms of key functions
‒ Enabling active participation by consumers to adjust consumption based on price and overall demand
‒ Better matching generation and demand
‒ Integrating renewable (e.g., solar, hydro, wind, etc.) and distributed power generation sources
‒ Providing more and better energy storage options
‒ Improving power quality, reliability, and enhancing resiliency: wide area situational awareness (WASA)
4
Demand
response
The ICT Framework in Smart Grid
5
Proposed ICT Framework
6
(Big) Data
Analytics
USEFUL INFO
RAW DATA
Information and Communication
Technologies (ICT)
Two-way communications
Networks in the ICT Framework
• Private networks: deployed by utility companies• Networks in the advanced metering infrastructure (AMI)
• Metering data gathering
• Demand response control message distribution
• Networks in the wide area monitoring systems (WAMS)• Monitoring data gathering
• Controlling message distribution
• Etc.
• Public networks: Internet based public network service• Remote monitoring and control from smart phones
• E.g., smart appliances that have Wi-Fi connection to the Internet
• Data transmission through cellular network service • E.g., transactional data from EVs
• Applying public cloud computing service• For big data analytics
• Etc.
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Advanced Metering Infrastructure (AMI)
8
Backbone
(Fiber optics)
Advanced metering infrastructure
(AMI) enables two-way
communication between utilities
and customers.
MDMS (Metering
Data Management
System)
9Source: http://sine.ni.com/cs/app/doc/p/id/cs-16856#prettyPhoto
Smart Appliances
10
Figure sources: http://insidebitcoins.com/news/bitcoin-and-the-economy-of-things/26486
http://news.filehippo.com/2013/03/smart-grid-the-future-of-home-appliances/
http://housesogreen.com/2012/08/review-lennox-icomfort-wi-fi-thermostat-sonos-for-air-conditioners/
http://www.isustainableearth.com/green-products/the-lowdown-on-modern-electric-vehicles
http://www.engadget.com/2015/02/03/irl-a-month-controlling-my-coffeemaker-over-wifi/
Security in the ICT Framework
11
Security Requirements
• Various types of data in smart grid communications have different security requirements as well as delay requirements
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Confidentiality Integrity Non-repudiation
Demand
Response
Pricing forecast X X
Energy forecast X X X
Metering data X X X
Wide Area
Monitoring System
Monitoring data X
Control message X X
Cloud ComputingPre-processed data X X X
Raw energy forecast X X X
External Sources Other information X
Security Mechanisms
• Authentication and key management process for private networks.
• Illustrated with DAPs in AMI as communication nodes13
Uninitialized Active neighbor AS�1 �2
�1 = request�|��1|��1||����1 + �1 + �1�
�2 = ��2||��2��1||��2||�2�||����2 + �2 + �2�
�′3 = ��1 ���1�|�1|��3�|��� |�!"||����2 +∗�
�3 = ��2��′ ′
3�||���3′′ �
�4 = �′3�|��2|��5||����2 + �5�
�5 = ���2∗ ���1�||�6||����1 + �6�
�′′3 = �′3||�4||���′3 + �4�
�6 = �1,2
Active DAP
AS
�2
�3
�4
�1 DAP Active DAP
Active DAP
Initial authentication process• A supplicant sends request to all of its active
neighboring nodes
• The active neighboring nodes relay the
request to the AS Detailed process
Security Mechanisms (cont’d)
• Security schemes in uplink and downlink transmissions
14
Security scheme for uplink transmission (single link).
Security scheme for uplink transmission (multi
link aggregation).
XOR
()
H*�*�
E* �)||
tAuthentication
Server
t
Security scheme for downlink transmission.
XOR
(+
H*�*�
E* �+||
tAuthentication
Server
t�+
E
Broadcasting messages Individual messages
Security Mechanisms (cont’d)
15
Security scheme based on zero-knowledge proof for the proposed
distributed learning technique.
Security Mechanisms
• For internal data transmission over the Internet
• Utility company does not have complete control
‒ Huge amount of sensitive data goes through uncontrollable networks (by power companies)
• How to protect such transmission?
‒ We proposed to use identity-based security schemes to assist existing security schemes in the Internet and cloud computing servers
‒ More efficient security parameter management
‒ More control on the side of utility company
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• Anyone in the domain can generate public keys of other parties
• Public keys are refreshed easily
Private Key Generation
Expiration timeIdentity
Public Key Generation
• Private keys are generated by private key
generator (PKG)
• Outdated private keys are easily revoked
Proposed ID-Based Signcryption
17
Preliminaries
Bilinear mapping:
• Bilinearity: for all and .
• Non-degeneracy: for any for all .
• Computability: there is a polynomial time algorithm for computing for all .
Proposed ID-based signcryption scheme
Controlled by utility companies
Analysis of the IBSC Scheme
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Modified Weil pairing is adopted to
apply performance analysis Weil pairing
over supersingular elliptic curve
Modified Weil pairing: ,
where and is a
primitive cube root of unity in .
Security analysis
The security of the proposed IBSC is based on the following computational problems
•Computational Diffie-Hellman Problem: given �, ,�, -�, .� ∈ 01, ∀,, -, . ∈ 34∗ , there is no
polynomial time algorithm to compute ,-� ∈ 01•Bilinear Diffie-Hellman Problem: given �, ,�, -�, .� ∈ 01, ∀,, -, . ∈ 34
∗ , there is no polynomial
time algorithm to compute 5̂ �, � 789 ∈ 0:
Performance analysis
# of
;<# of
mul
# of
=>
# of
=?
# of
=@
Signcrypt 1 5 1 2 1
Decrypt 1 0 0 0 1
Sign 0 3 1 0 0
Verify 3 1 1 1 0
A = ?BC -DE = ?BC -
A = ?BC -DE = B>? -
A = @FB -DE = ?BC -
A = @FB -DE = B>? -
Signcrypt 39.59 ms 68.89 ms 45.4 ms 74.7 ms
Decrypt 7.44 ms 7.44 ms 13.25 ms 13.25 ms
Sign 19.29 ms 36.87 ms 19.29 ms 36.87 ms
Verify 28.75 ms 34.61 ms 46.18 ms 52.04 ms
G ≡ 2 IJK 3G = ,L − 1, for some prime Gand positive integer ,�N is the multiplier in 01
Intel Core
i5 @ 3.1
GHz & 8G
RAM
Possible Applications of the Proposed IBSC Scheme
• Short message encipherment
• Digital signature
• Session key distribution
• Signing right delegation
19One to one
One to many
Signing right delegation• When a local control center is under maintenance
• Not available due to cyber attack/natural disaster
• Etc.
Future Research Directions
20
Future Research Directions
• Optimization of renewable power source deployment and operation
• To better accommodate the management of fossil fuel based power sources
• Fast and reliable learning techniques for big data analytics
• Power consumption analysis
• Smart pricing analysis
• Real time anomaly detection (e.g., PMU data)
• Large scale graph modeling techniques
• To better analyze the relationship among pieces of information
• Parallel computing and cloud computing
• To speed up computation
• To enhance scalability
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Future Research Directions (cont’d)
• Better integration of EVs
• Fast vehicular network for data exchange
• (Near) real-time big data analytics for EVs
• Cyber security
• Fast privacy protection
• without traditional encryption algorithms
• Or fast encryption algorithms
• Real-time data integrity protection
• Real-time anomaly detection, etc.
• Cloud security
• Etc.
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Conclusion
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Conclusion
• Smart grid is a massive cyber physical system
• Advanced ICT are applied in smart grid communication infrastructures
• Smart grid generates big data
• Big data analytics is important to DR, monitoring system, and other applications in smart grid
• Many issues remain open in big data research of smart grid communications
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AcknowledgementThis work was supported by the National Science Foundation under the grant CNS-1423408
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[11]. F. Ye, Y. Qian, R. Q. Hu, and S. K. Das, “Reliable Energy-Efficient Uplink Transmission for Neighborhood Area Network in Smart Grid”, IEEE Transactions on Smart Grid, Vol.6, No.5, pp.2179-2188, September 2015.
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[20]. S. Xu, Y. Qian and R. Q. Hu, “A Secure Data Learning Scheme in Big Data Applications”, Proceedings of The 25rd International Conference on Computer Communications and Networks (ICCCN 2016), August 1-4, 2016, Waikoloa, Hawaii, USA.
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