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# A Survey on Synchrophasor Data Quality and Cybersecurity Challenges, and
Evaluation of their Interdependencies

Aditya Sundararajan  Tanwir Khan  Amir Moghadasi  Arif I. Sarwat

###### Abstract

Synchrophasor devices guarantee situation awareness for real-time monitoring
and operational visibility of the smart grid. With their widespread
implementation, significant challenges have emerged, especially in
communication, data quality and cybersecurity. The existing literature treats
these challenges as separate problems, when in reality, they have a complex
interplay. This paper conducts a comprehensive review of quality and
cybersecurity challenges for synchrophasors, and identifies the
interdependencies between them. It also summarizes different methods used to
evaluate the dependency and surveys how quality checking methods can be used
to detect potential cyber-attacks. In doing so, this paper serves as a
starting point for researchers entering the fields of synchrophasor data
analytics and security.

###### Keywords:

Synchrophasors, data quality, cybersecurity, methodologies.

##  1 Introduction

Smart grid has complex dependencies between physical and cyber realms mqtt ;
theft15 ; theft18 ; theft16 . This has been demonstrated by recent attacks on
smart grid (Table 3) dagle ; corsi ; larsson ; berizzi ; gomes ; imai . These
attacks exploited a limited visibility of the system and inadequate support
from reliability coordinators theft17 ; theft22 ; bhatt ; nerc1 ; zima ;
theft12 ; ashton ; naspi1 ; gomes2 ; theft19 ; theft20 ; theft21 . Wide-area
measurement systems (WAMS) increase the situation awareness (SA) for operators
sutar ; narendra ; novosel . WAMS devices that are part of the wide area
monitoring, protection, automation and control include phasor measurement
units (PMUs) at transmission, frequency disturbance recorders (FDRs) at low-
voltage distribution and micro-PMUs ($\mu-$PMUs) for distributed renewables,
called synchrophasors gentz ; reddy ; amare ; theft13 ; theft14 ; negash ;
rahman ; nazari ; chouhan ; micropmu .

Significant challenges to the implementation of synchrophasors have emerged in
communication, data quality and cybersecurity. The existing communication
infrastructure is slow, expensive and inflexible. To leverage SA and support
timeliness, adequate quality checking methods must be in-place at the phasor
data concentrators (PDCs) which aggregate and process raw data and flag
corrupt data. Due to their ubiquity, synchrophasors have an increased attack
surface. The applications and challenges of synchrophasors are well-researched
sexauer ; singh ; sanchez ; tushar ; lauby ; mohanta . However, the challenges
of data quality and cybersecurity are considered one independent of the other,
when in reality, they are interdependent pmudqnaspi ; christo ; zhu ; yangbo ;
nadia ; veda ; sabha ; danielson2013analysis ; huyi ; kdjones ; huang ; huang1
; gpsspoof ; morris ; cyberlikeli ; cyberthreats ; hlin ; beasley ; tiago ;
energies ; auth01 ; auth02 ; auth03 ; attack01 ; attack02 ; doesecchal ;
pmuchal ; re10 . Further, the literature does not leverage the knowledge of
one challenge to address the other. For example, studying the changes to data
quality can be key to potentially identify an underlying attack vector or an
unexploited vulnerability.

The main contributions of this paper are: (1) maps the dependencies between
data quality and cybersecurity challenges of synchrophasors; (2) reviews the
methods to evaluate the challenges; and (3) surveys how data quality checking
methods can leverage their observations to detect issues related to security.
The paper also provides a high-level overview of synchrophasors, their
standards, key applications, and challenges johnson ; martin ; ali ; retty .
It is key to know that poor quality can be due to device errors or
communication challenges like congestion and packet collision. Similarly, all
cyber-attacks do not impact the data, although reduction in quality is one of
the biggest observable consequences of a successful attack. A layout of WAMS
comprising synchrophasors is shown in Fig. 1. This paper explores the
challenges for PMUs at transmission and FDRs at distribution level.

Figure 1: Layout of smart grid WAMS comprising PMUs, $\mu-$PMUs, FDRs and PDCs
Figure 2: The proposed structure of this survey paper

This survey paper considers data quality and cybersecurity as challenges,
where each has different issues. Issues are the ways in which the particular
challenge manifests when observed. Figure 2 maps the challenges to their
corresponding issues. The challenge of quality manifests in three ways: noise,
outliers and missingness. Noise can be due to logical inconsistencies in data
values or attributes while outliers result from poor integrity and
origination. Missing data is a direct consequence of poor completeness and
availability. Accuracy is impacted by noise, outliers as well as missingness
while plausibility is a characteristic impacted by noise and outliers. These
characteristics are discussed in Section 3.1. Cybersecurity manifests as
delay/loss, manipulation or theft. While a delay/loss corresponds to a packet
delay or drop due to congestion, timeout, buffer fullness or an intentional
attack that affects availability, manipulation deals with attacks that alter
the information, thereby impacting integrity. Theft captures attacks which
compromise the confidentiality of data such as snooping, spoofing or
espionage. These attacks occur at different levels of the synchrophasor
hierarchy: device corresponds to the edge devices like PMUs, FDRs, or
$\mu-$PMUs, while aggregator implies Local PDCs or SuperPDCs. Communication
refers to the synchrophasor network while Application contains the different
power system applications that use synchrophasor data.

The rest of the paper is organized as follows. Section 2 summarizes the
architecture, major applications and key challenges of synchrophasors. The
characteristics are described in Section 3, and their interdependencies mapped
in Section 4. While evaluation methods for data quality and cybersecurity are
discussed in Section 4.1, Section 4.2 surveys methods which use data quality
characteristics to detect potential cyber-attacks. Section 5 highlights future
directions of research in synchrophasor data analytics and cybersecurity.

##  2 Architecture, Applications, Challenges

Synchrophasors can be standalone devices with dedicated purposes, or be a part
of a larger system like the substations, depending on various functional and
operational requirements. With increased penetration of renewables and smart
loads, synchrophasors are used at distribution transformers and points of
common coupling to study frequency disturbances and harmonics. The
architecture of synchrophasor devices are summarized at the device and network
levels below.

PMU device: It comprises Current Transformers (CTs) and Potential Transformers
(PTs) that measure current and voltage magnitudes which are then converted to
digital data, a microprocessor module that compiles these values, computes
phasors, and synchronizes them with the Coordinated Universal Time (UTC)
standard reference used by global positioning system (GPS) receivers that
acquire a time-lag based on the atomic clock of GPS satellites premerlani ;
ieee2 ; sutar ; phadke ; rihan . They measure local frequency and its rate of
change, and can record individual phase voltage and current along with
harmonics, negative and zero sequence values eval4 . The information paints a
dynamic picture of the grid at a given time. PMUs and PDCs transmit measured
data as frames ieee3 . A $16$-bit cyclic redundancy check ensures data
integrity. PDCs equipped with logging functionality use comma separated values
or transient data exchange for data logs, and Common Format for Event Data
Exchange for event logs kezunovic ; ieee4 . The data transfer rate of PMUs,
which determine the message processing delays and network latencies, depend
greatly on the timing requirements of applications.

PMU network: If there are multiple PMUs in a substation, Local PDCs aggregate
site-level data and then transmit to a SuperPDC. PDCs conduct various data
quality checks and set flags according to the issues encountered, log
performance, validate, transform, scale and normalize data, and convert
between protocols ieee1 . There is typically a direct interface between PDC
and the utility’s SCADA or energy management system. PDCs can be deployed as
standalone devices or integrated with other systems in the grid.

FDR device: The Oak Ridge National Laboratory and the University of Tennessee
Knoxville have been leading the FNET/GridEye project since 2004. FDRs have
been installed and managed to capture dynamic behaviors of the grid. Although
FDRs are essentially PMUs, they are connected at $120V$, and hence incur lower
installation costs than traditional PMUs do chai . FDRs are largely deployed
at renewable integration zones of the grid, and measure nearly $1,440$ samples
per second with a hardware accuracy of $\pm 0.5mHz$ while PMUs measure between
$10$ and $240$ samples per second and use GPS receivers that have $1\mu$s
accuracy for synchronization egypt ; liu ; stenbakken ; wang . Given the
availability of an extensive discussion of the architecture by the author of
fdrdev ; fdr3rd , it is beyond the scope of this paper.

FDR network: FDRs use the internet to send data directly to the central
servers for analytics and can provide information on transients, load
shedding, breaker reclosing and the switching operations of capacitor banks
and load tap changers wang . Unlike PMUs, FDRs can be installed at buildings
and offices.

Synchrophasor standards: Multiple standards exist for PMU data measurement,
transfer and communication, proposed by IEEE, the National Institute of
Standards & Technology (NIST), the North American Electric Reliability
Commission (NERC) and the International Electrotechnical Commission (IEC)
ieee5 ; ieee6 ; ieee7 ; firouzi ; mackiewicz ; iec1 ; nistir ; cip . Due to
multiple specifications and guidelines, there are possible contradictions in
recommendations mehta ; johnson ; martin ; ali ; retty . A North American
SynchroPhasor Initiative (NASPI) report in early 2016 identified the need for
standardizing definitions related to synchrophasor data quality and
availability by establishing the PMU applications requirements task force
(PARTF) naspi16 . IEEE standard C37.X deals with WAMS, specifically PMUs ieee1
; ieee8 ; ieee9 . These standards are summarized in Table 1 with their core
contributions highlighted. A more comprehensive review of the synchrophasor
standards is documented in synchstd .

Table 1: Various standards and guidelines for Synchrophasors Body |  Standard |  Core Contribution
---|---|---
IEEE |  1344-1995 |  Original parameter definitions for synchrophasors
|  C37.118-2005 |  Improved message formats, inclusion of time quality, Total Vector Error (TVE)
|  C37.239-2010 |  PMU/PDC event logging
|  1711-2010 |  Serial SCADA Protection Protocol for substation serial link cybersecurity
|  C37.118.1-2011 |  PMU measurement provisions, performance requirements
|  C37.118.2-2011 |  Synchrophasor data transfer requirements
|  C37.238-2011 |  Common profile for applying Precision Time Protocol (PTP) using Ethernet
|  C37.242-2013 |  Synchronization, calibration, testing and installation of PMUs for PC
|  C37.244-2013 |  PDC functions and requirements for PC and monitoring
|  C37.111-2013 |  PMU/PDC data logging using COMFEDE
|  1686-2013 |  Procuring, installing and commissioning IED cybersecurity
|  C37.240-2014 |  Sound engineering practices for high cybersecurity of substation APC
IEC |  61850 |  Interoperable and adaptable architectures to substation automation
|  61850-90-5 |  Requirements for data exchange between PMUs, PDCs, PCs and control center
|  62351-1,2 |  Security threats and vulnerabilities in smart grid devices
|  62351-6 |  Prescribes digital signature using asymmetric cryptography for sending PMU data
NERC |  CIP 002-009 |  Series of standards to ensure enterprise, field and personnel security
NIST |  NISTIR 7628 |  Provides guidelines for smart grid cybersecurity (including WAMS)

Applications: Synchrophasors streamline security, reliability and stability of
power systems. They have online and offline applications smartcity . Online
applications of PMUs include enhancing real-time SA, analyzing faults and
disturbances, detecting and appraising oscillations and harmonics that impact
power quality, and improving accuracy and reducing computational time of state
estimation. Offline applications include congestion management, providing
effective protection schemes, benchmarking, system restoration, overload
monitoring and dynamic rating, validating the network model of SCADA, and
improving overall power quality novosel ; iotsg ; appmag . Real-time (online)
applications of FDRs include frequency monitoring interface integrated with
command and control centers in the future for power system health diagnosis to
prevent cascading failures, and event trigger module that detects and notifies
the mismatch between generation and load caused by frequency variations.
Offline applications include event visualization that renders the data read
from the even data files zhangfnet .

Challenges: One of the major drawbacks of synchrophasors is the lack of
transmission protocol, which makes them vulnerable to spoofing attacks gentz .
The existing architecture is not scalable since it entails an initially high
investment. NASPI’s research initiative task force (RITT) emphasizes optimal
placement as a significant challenge but also one dependent on the nature of
applications the utility intends to use them for naspi1 . The literature has
multiple models including but not limited to genetic algorithm, simulated
annealing, Tabu search, Madtharad’s method, particle swarm optimization,
artificial neural networks, binary search and binary integer programming to
address this challenge reddy ; amare ; negash ; rahman ; nazari ; chouhan ;
yuill ; manousakis . More recently, managing and analyzing large volumes of
synchrophasor data has become increasingly challenging. Lack of standardized
data management solutions for smart grid has only made this problem more
challenging. The ubiquitous presence of these devices has expanded their
attack surface, making them vulnerable to different types of attacks. These
two challenges are elaborated in the following section since they percolate to
applications that directly operate upon the streaming data subject to minimal
processing owing to timeliness requirements.

##  3 Data Quality and Cybersecurity Challenges in Synchrophasors

Due to their wide-ranging communication and automation capabilities, the
challenges of synchrophasor data quality and cybersecurity have gained
prominence.

Table 2: Summary of existing research in Synchrophasor Data Quality Challenges and Solutions Attribute |  Challenges |  Solutions
---|---|---
Completeness (Device, aggregator) huyi ; pmudqnaspi ; wuh  |  • PMU/PDC device damage • Faulty PMU-PDC communication • Network error • Database storage error • Data missing for failing to comply with latency and QoS requirements |  • Acquiring better management techniques • Use of TCP protocol to re-transmit the lost data packets at the cost of timeliness • Adjusting the synchrophasor frame rate by increasing the wait time at PDC
Measurement Accuracy (Device) pmudqnaspi ; christo ; terzija ; wuh ; cokkinides ; stenbakken1  |  • Expected signal differs from measured signal due to harmonic interference • Introduction of noise to data |  • Improving phase error using filtering techniques • NIST calibration per Standard C37.118-2005 • Omnidirectional antennas • Context-based reconstruction of missing data • Network Time Protocol (NTP), e-Loran and Chip Scale Atomic Clock (CSAC)
Attribute Accuracy (Device, aggregator, communication) pmudqnaspi ; zhu  |  • Measured vs actual timestamp discrepancy due to satellite timing error; disagreement between encoded and actual location of PMU |  • Development of linear state estimation tools • Avoiding timestamp discrepancy by modifying real-time clock element • Using OMP-based identification, BB algorithm to solve PMU location discrepancies
Plausibility and Availability (Communication, application) veda ; sabha ; kdjones ; heiko ; maab ; danielson2013analysis  |  • Impact of measurement system on individual data points • Data inaccessibility due to high network latency or device failure. • Delayed data arrival due to increased routing traffic |  • Use of Electrical Data Recorder (EDR) tools for capturing high-rate time series data, data storage and analysis • A more lenient time limit could be set for non-critical application usage. • Latency-aware application design
Origination (Device, aggregator, communication, application) pmudqnaspi ; castello ; dua ; huang1  |  • Poor standard interpretation, implementation • Misalignment, erroneous compression • Latency, loss of communication nodes • Data corruption due to delivery time of PDC exceeding permissible slot • Network unavailability to process incoming data streams |  • Redundancy in communication by using wireless and wired connections • Lagrange interpolating polynomial method • Data substitution, imputation, interpolation and extrapolation • Stochastic forecasting with Prediction Error Minimization (PEM)
Logical Consistency (Aggregator, communication) pmudqnaspi  |  • Data transmitted contains no headers • Sampling rate of data changed at PMU without being adjusted at PDC • Data duplication while processing • Data from different PMUs with incorrect timestamps |  • Logical consistency can be ensured by maintaining the PMU registries and data protocols

###  3.1 Data Quality Challenges

NERC’s real-time tools best practices task force (RTBPTF) and NASPI’s PARTF
impose requirements to ensure synchrophasor data quality pmudqnaspi ;
pmudqnerc . Data quality can be contextualized in different ways, depending on
the needs of the concerned domain. For instance, data quality requirements of
a smart meter recording energy consumption might differ from those of a net
meter at a solar photovoltaic (PV) power plant. NASPI contextualizes
synchrophasor data quality to determine “fitness of use” in terms of accuracy
and lineage for static data points; lineage, completeness and logical
consistency for static datasets; and availability, timeliness and origination
for streams of data points pmudqnaspi .

There could be different causes for poor data quality: a) Device: poor
calibration of device, biases due to CT, PT; erroneous filter design, poor
synchronization of timing measurements, and issues due to measurement channel;
b) Communication: latency exceeding stipulated limits, network congestion,
signal interferences and failure of communication nodes; c) Aggregator: Data
transformation resulting in errors, delayed arrival of packets dropped due to
time-limit exceeding, and unwanted duplication or corruption of data during
computations; and d) Application: storage and maintenance issues, insufficient
training size, erroneous manipulations to the data and poor association of
context. Although data quality requirements vary with applications, they have
been extensively documented synchstd ; huang ; pmudqnaspi ; pmudqnerc . The
existing literature on synchrophasor data quality is summarized in Table 2.

Completeness: focuses on the gaps between different values, accounts for
missing values pmudqnaspi . The attributes of completeness defined at device
and aggregator-levels are: gap rate\- number of gaps in data per unit time;
mean gap size\- mean of the length of known gaps; and largest known gap\-
length of the largest known gap among the different gaps. While completeness
is impacted by device malfunction, packet drops and communication link
failure, the literature does not recognize the possibility of an attack behind
such causes.
Accuracy: can be of the value or attribute, primarily measured in Total Vector
Error (TVE), which according to IEEE standard C37.118, is the vector
difference between the measured and expected phasor value (magnitude, angle
and frequency). Accuracy is categorized into that of: data values\- impacted
by factors like the difference between expected and observed signals or the
introduction of noise to the data within the synchrophasor; and data
attributes\- affected by factors like accuracy of the measured timestamp,
agreement between encoded and actual location coordinates of the device, and
alignment of the location recorded in the power system topology with its
actual location nadia .

Plausibility and Availability: Measurement specifiers are the attributes of
data which describe whether the process of measuring some phenomenon of the
power system (observed value) and calculating its value (expected value) are
documented effectively in terms of standard units to a given precision and are
within a stated confidence interval nadia ; sabha . These specifiers have
decisive sub-attributes influencing the qualitative value of data: data
representing the measurement of quality or condition of the grid, and data
represented in the form of SI units up to 3 decimal places with a confidence
interval included.

Network availability plays an important role in streaming data
danielson2013analysis , and in-turn affects data availability. In case of high
network latency, the incoming data streams from different synchrophasors get
delayed or lost, causing applications to perceive them as missing or
incomplete. Hence, network availability can be considered an indirect
attribute affecting quality. This can be mitigated if the overlying
applications are programmed to account for the delays, or if a more lenient
waiting time limit is set. However, the second solution is dependent on the
kind of applications the synchrophasors cater to. The latency requirements for
synchrophasors recommended by the standards are very stringent.

Origination: is the source from which the data is measured. Its
trustworthiness is associated with the background and source. Its attributes
are: point of origin: the class of device from where the data originated
(Measurement (M) or Performance (P) for PMUs), the standard followed by the
device, and any data manipulation or standardization techniques through which
the data passes eval5 ; pmudqnaspi ; coverage: physical location of the device
based on its geospatial or electrical topology location zhu ; yangbo ; and
transformation applied to the data at the device, aggregator or application
level.

Table 3: Summary of the Recent Cyberattacks on Smart Grid Impacting Data Quality Source of Attack (Year) |  Target of Attack |  Data Quality Characteristic Impacted |  Cybersecurity Characteristic Impacted |  Attack Specifics
---|---|---|---|---
Vulnerability in network firewall (2001) |  California ISO (CAISO) web servers |  Consistency, accuracy |  Integrity |  Poor security configuration during planned maintenance
Stuxnet worm (2010) |  Programmable Logic Controllers (PLCs) at SCADA |  Accuracy, consistency, plausibility |  Integrity, Availability |  Exploits zero-day vulnerabilities of PLCs
BlackEnergy (2011) |  Human-Machine Interface of utility grid control systems |  Plausibility, origin, accuracy, consistency |  Confidentiality, Integrity, Availability |  General Electric’s HMI targeted
Remote Access Trojan; watering-hole attack (2014) |  ICS/SCADA |  Plausibility, origin, accuracy, consistency |  Confidentiality, Integrity, Availability |  Conducted by Dragonfly, Energetic Bear
Trojan.Laziok reconnaissance malware (2015) |  Energy companies |  Origin, plausibility |  Confidentiality |  Gathered information from compromised devices
BlackEnergy3 (2015) |  Ukrainian grid control center |  Plausibility, origin, accuracy, consistency |  Confidentiality, Integrity, Availability |  Lack of SA left 220,000+ customers without power
WannaCry ransomware cryptoworm (2017) |  Computers running MS Windows Operating System |  Availability, origin |  Availability |  Used EternalBlue, a vulnerability in older Windows systems

Consistency: determines how agreeable the data is with the overall structure
of its type. Incompatibility of attributes in terms of measurement rates or
header labeling between datasets results in outliers, leading to an
inconsistent result from an application. The attributes of consistency are:
Header frame consistency: consistency of the Header frame of the device. This
could be categorized into: persistence of PMU Header that states whether the
PMU header structure is consistent over time, and persistence of PDC Header
that states whether the PDC header structure is consistent over time; Data
frame consistency: consistency of Data frames of the device. This could be
categorized into: persistence of PMU Data frame that states whether the PMU
data frame structure is consistent over time, and persistence of the PDC Data
frame that states whether the PDC data frame structure is consistent over
time; order consistency of Data frames: whether the order in which the Data
frames are recorded is consistent in the device; consistency in compliance to
standards recommended for PMU and all the devices associated with it; and
consistency of reporting rate: whether data reporting rate is consistent
across all devices.

Emerging research in this area has lately focused on determining solutions for
ensuring data quality. These solutions include using omnidirectional antennas
to improve GPS availability, context-aware determination of missing data
streams using accurate timing information, Network Time Protocol (NTP) and
associated chip scale atomic clocks (CSACs) as backups for synchronization
when GPS fails, imputation, interpolation and extrapolation, stochastic
forecasting with prediction error minimization (PEM) and data substitution
huang .

Evaluation of Quality: Methods to evaluate quality is discussed in Section
4.1. The approach for performance evaluation is to first study the impacts of
device calibration and network conditions on quality, then examine how poor
quality reduces the application performance pmudqnaspi . Two effective methods
are proposed to evaluate the impact of quality on performance: benchmarking
that tests an application multiple times with numerous erroneous datasets in
contrast to those with no known errors, and standardization that documents,
for each application, the level of tolerable errors.

###  3.2 Cybersecurity Challenges

Table 4: Summary of Existing Research in Synchrophasor Cybersecurity Challenges and Solutions Level |  Challenges |  Solutions
---|---|---
Device, Aggregator synchstd ; flexpdc ; wuh ; cip ; gpsspoof ; morris  |  • Device damage • Device calibration tampering • Forging PMU data • GPS spoofing |  • Multi-alteration technique to trace adversary in event of GPS spoofing • Visible GPS satellite prediction • Anomaly between expected and measured GPS signals • Using SSL/TLS or IPSec to encrypt data before transmission • Using state estimation technique to mitigate device calibration and tampering • Rigorous penetration testing prior to installation
Communication cokkinides ; stenbakken1 ; zhushi ; heiko ; maab ; cyberthreats ; energies ; auth01 ; attack02  |  • Denial of Service • Man-in-the-Middle • False Data Injection • Snooping attack • Delay attack |  • Airgapping PMU network • Filtering routers, disabling IP broadcasts, applying security patches, disabling unused ports • Server authentication by clients before establishing connection • Use of time-series state estimation • Cryptographic methods like AES, DES • Mutual authentication • Cyber trust model with blockchains • NASPInet hub-spoke model • Optimal key generation and distribution
Application castello ; dua ; auth02 ; auth03 ; attack01  |  • Phishing and social engineering • APT and insider threats • Replay attacks |  • Authentication, Authorization and Accounting (AAA) • Use of secure data transfer protocol to prevent replay attack

Synchrophasors cater to applications like state estimation, contingency
analysis and optimal power flow that need real-time high-resolution data
measurement, communication and analytics inlsec . Therefore, a successful
attack on these devices might cause erroneous SA or cascading failures
synchrodqup ; cyberlikeli . Yet, many industrial organizations do not consider
synchrophasors as critical cyber assets. Recent cyberattacks on the smart grid
(Table 3) mostly used powerful malware like worms, viruses or Trojan horse,
but a few attacks like the one on the Pacific Gas & Electric transmission
substation relied on physical means. These attacks jeopardized not just the
availability of power but also that of control data (information).
Cybersecurity of synchrophasors are categorized into: 1) Device, Aggregator,
2) Communication, and 3) Control center application.

Device, Aggregator: NASPI Network (NASPInet) is logically capable of
integrating WAMS across multiple geographically distant organizations using
phasor gateways (PGWs). The attacks at this level compromise data integrity,
targeting devices from individual PMUs to PDCs, SuperPDCs or even PGWs. Some
attacks include: 1) tampering the signal measurement units of devices through
interference, 2) illicitly changing the calibration of devices to report
erroneous readings, 3) forging data to reflect wrong measurements, and 4) GPS
spoofing by broadcasting fabricated signals to the receiver to yield erroneous
synchronization of phasors computed, modifying satellite position, or
replaying legitimate GPS signals at later timestamps gpsspoof .

GPS spoofing can be mitigated by enabling the receiver to predict visible GPS
satellites at a given position and time instant and use the Coarse/Acquisition
(C/A) code from those satellites. Another strategy compares the measured GPS
signal to the estimated signal and computes the anomaly error which must have
an accuracy of $\leq 40ns$ for nearly $95\%$ of the values according to IEEE
C37.118 zhu . Synchrophasors must be subject to rigorous testing before
installation. Some methods include port scans, device security feature
robustness, network congestion testing, denial of service testing, network
traffic sniffing and disclosure testing morris . These tests should be
periodically conducted by certified white hat penetration testers after
installation. Regular patches and configuration updates must be made down to
the end-device level.

Table 5: Summary of the Interdependency between Quality and Cybersecurity Challenges Level |  Quality Attribute |  Quality Issue |  Cyber-Attack Observed |  Security Attribute Impacted
---|---|---|---|---
Device |  Completeness, accuracy, plausibility |  Synchronization signal loss, measurement signal loss, missing data |  GPS spoofing, replay, device tamper, changing device calibration, FDI |  Integrity
Aggregator |  Origin, consistency, plausibility |  Corrupted data, anomalies, outliers |  FDI, Tampering, Buffer overflow, MITM |  Confidentiality, Integrity
Communication |  Availability, origin, consistency |  Anomalies, outliers, inconsistent, out-of-order data |  DoS, MITM, FDI, snooping, replay, delay |  Confidentiality, Integrity, availability
Application |  Origin, availability, consistency, completeness, accuracy |  DoS, delay, APT, FDI, theft/fraud, insider attack |  corrupted data, missingness, anomalies, outliers |  Confidentiality, Integrity, Availability

Communication: Synchrophasors support bidirectional communication channels,
where data measurements flow from devices to the control center while control
signals flow the other way. The vulnerabilities of the protocols used by the
devices also contribute to the overall security. Attacks on communication
channels compromise integrity, availability and confidentiality. Some attacks
include: 1) Denial of Service (DoS) by overwhelming PMUs, PDCs or other
aggregation devices higher in the hierarchy with bogus frames so that
legitimate frames are lost, delayed, denied or dropped, 2) Man-in-the-Middle
(MITM) attacks by a malicious entity posing itself as PDC (to PMU) or PGW (to
PDC) and sending malicious commands that causes PMUs/PDCs to behave in an
abnormal manner that triggers failures, 3) False Data Injection (FDI) by
intercepting frames over the channel, altering or replacing them with
malicious information that then gets propagated to higher levels of the WAMS,
4) Snooping by the attacker eavesdropping on the channel for incoming or
outgoing frames, typically not modifying or stealing but just capturing a copy
of that information for packet replay or espionage, and 5) Delay caused by
compromising communication routers that deliberately induce latencies in
propagation to critically affect the grid’s SA.

Many authentication and authorization algorithms are proposed to secure
synchrophasor data over communication channels cyberthreats . These methods
range from conventional encryption methods to cyber trust. Due to the ubiquity
and widespread range of these devices, key distribution and management becomes
a problem. Mutual authentication is also proposed to account for trust
energies . Decentralized, blockchain-based trust acquisition is being
considered too. The publish-subscribe hub-spoke architecture proposed by
NASPInet supports dynamic sharing of device data to alleviate shortcomings of
the communication medium like delays and latencies. Standards like IEC
61850-90-5 recommend trusted key distribution center to generate and
distribute keys that meet system requirements auth02 ; auth03 ; attack01 ;
attack02 .

Application: Despite being protected by enterprise security tools for
intrusion detection and prevention, virtualization, segmentation,
authentication, authorization and access control, cyberattacks still
proliferate doesecchal ; pmuchal . It is understood that any successful attack
at the other two levels perpetrated in a manner undetectable by the enterprise
security systems can pose a significant threat. The attacks at this level are
the most dangerous, since crucial power system applications use data from WAMS
to conduct analysis to address reliability, power quality, network topology,
and faults. An adverse impact on these calculations could compromise the
“self-healing” nature of the grid. More recent solutions include game theory,
machine learning, proactive data visualization, and Defense-in-depth theft22 ;
human .

####  3.2.1 Evaluation of Security

Works have tested the resilience of PMUs and PDCs against different attacks.
The authors in cybersec1  conducted penetration testing of a synchrophasor
network in IEEE 68-bus system to map vulnerabilities against the Common
Vulnerabilities Exposure (CVE) database. Potential corrective measures to
ensure the security of PMUs and PDCs is proposed cybersec2 . Considering the
security at substation and information levels, the authors provide a wide
range of tools to mitigate breaches at both fronts. A multilayered
architecture at the substation is proposed where different levels of data
abstraction is provided between PMUs and external environment, supplemented by
firewalls, User Datagram Protocol (UDP) Secure for communication over
untrusted networks, and remote access using Secure Shell (SSH).

##  4 The Data Quality-Cybersecurity Dependency

The severity of an attack can be understood from the extent of its impacts on
the targeted system. With the smart grid encouraging interoperability between
devices, information, applications, and protocols, a transparent and direct
information exchange is now feasible. This also means that if information in
one of the interconnected systems is infected, it is bound to propagate to
other systems upon exchange, affecting the whole network. Synchrophasor
devices harbor such vulnerabilities, as summarized in Section 3.2. However, to
mitigate cyberattacks on interconnected systems, the relationship between
devices and data must be known.

Table 6: Summary of Evaluation Methods for Quality (DQ) and Cybersecurity (CS) Issues Issue |  Challenge |  Evaluation Methods
---|---|---
Noise (DQ) |  Consistency, accuracy |  • Cable configuration, testing, validation • Specifying confidence interval, precision, TVE, ROCOF for measurements • Evaluating instrumentation channels – Model-based correction – State estimation-based error filtering • Presistence in Data/Header frames • Standards compliance
Outlier (DQ) |  Consistency, origin, accuracy |  • Standardization, benchmarking • Enhancing endpoints with switches, routers • Specifying device model, coverage and content
Missingness (DQ) |  Completeness, availability, accuracy |  • Dedicated communication channels • Enhancing endpoints with switches, routers
Delay/loss (CS) |  All levels |  • Regular penetration testing of all levels • Link-level encryption, selective encryption • Dedicated communication channels • Data redundancy for fault tolerance
Manipulation (CS) |  Device, Aggregator, Communication |  • Regular penetration testing of all levels • Link-level encryption, selective encryption • Data abstraction, multi-layered architecture • Data redundancy for fault tolerance • Augmenting ID/IPS, firewalls, ACLs, VPNs
Theft (CS) |  Device, Aggregator, Communication |  • Regular penetration testing of all levels • Data abstraction, multi-layered architecture • Data redundancy for fault tolerance • Augmenting firewalls, ACLs, VPNs

Table 5 summarizes key interdependencies between the two challenges. There is
a tight coupling between data quality and cyber-attacks, implying it is wise
to study synchrophasor cybersecurity by accounting for the impacts on quality.
In most attacks, plausibility, completeness, accuracy and consistency are
primarily impacted anurag ; kzhu . In Section 4.1, specific evaluation methods
for quantifying this relationship are reviewed. Section 4.2 looks at how data
quality characteristics can be used as markers to detect potential cyber-
attacks within the context of synchrophasors. Results from these subsections
are summarized in Tables 6 and 7, respectively.

###  4.1 Interdependency Evaluation Methods

Next to communications, cybersecurity was found to impact the design and
installation costs for synchrophasors eval2 . This is because they are
critical to the mission-support systems of the grid. Different practical ways
for utilities to mitigate quality issues like accuracy, timeliness and
consistency are also identified. Some methods include employing dedicated
communication channels between PMUs and PDCs, encrypting PMU data before
communication, and enhancing communication endpoints using firewalls and
routers. The report, however, does not delve into the details of how such
methods could impact latency (and hence, timeliness) and availability of the
data.

Given different manufacturers of devices, there will be differences in
measurement and calibration quality despite adhering to the standards. The
varying application requirements cause differences in application-level PMU
performance, of which data quality is a major one. The static and dynamic PMU
testing efforts of the Performance and Standards Task Team (PSTT) of NASPI and
the PMU performance characterization are briefly summarized in eval6 . In it,
the different steady-state tests performed on magnitude, phase and frequency
evaluate their conformance to accuracy requirements, which is an important
attribute of data quality and is a direct target of many cyberattacks. Given
the impact of instrumentation channels on the quality, they have been well-
characterized and evaluated for impacts on accuracy in the literature. The
errors induced by them could be rectified through model-based correction
algorithms and state estimation based error filtering. Some other avenues
where data quality could be evaluated include the cable configurations,
testing and validating the devices to ensure accurate, consistent performance
and interoperability at all levels pstt1 ; pstt2 . Although not explicit,
these works hint at the improvement in the resilience of synchrophasor devices
against potentially malicious activities by accounting for proper testing
methods to characterize and evaluate the different sources of errors prior to
deployment that might contribute to poor quality.

Table 7: Summary Showing How Quality Can Help Identify Cybersecurity Issues Cyber-attack |  Quality Affected |  Quality Check Looks For |  Mitigation Methods Using Quality
---|---|---|---
Device tampering (delay/loss, theft)
exploringchallenges ; synchsec  |  Completeness, plausibility, accuracy, consistency, origination |  Large gap sizes, inaccurate readings, ping fail |  • Statistical substitution: regression, imputation, interpolation • Intelligent substitution: neural networks, logistic regression, optimization • Securing the physical devices
Spoofing PMU data (manipulation)
fdi2 ; dataspoof ; dataspoof2  |  Consistency, accuracy, plausibility |  Unexpected values, errors, mismatch with SCADA values, redundant timestamp, out-of-order packet arrival |  • Monitoring line impedances for anomalies • Divergence and miscorrelation between SCADA and PMU data
GPS Spoofing (manipulation, delay/loss)
gpsspoof ; eval3 ; gpsspoofing1 ; gpsspoofing2  |  Consistency, origination, plausibility |  Inaccurate timing value, TVE $>1\%$, packets arrive out-of-order |  • Using multiple synchronization sources or telecommunications • Anti-spoofing checking methods at receivers • Internal holdover oscillators as backups for providing accurate timing signals • Spoofing match algorithm with Golden Search for lighter computation
Denial of Service (delay/loss)
synchsec  |  Completeness, accuracy, consistency |  Congestion at PDCs/network, delayed arrival of packets, dropped packets, inability to reach suspected device |  • Augmenting PDCs with inline blocking tools • Employ port hardening and disable IP broadcasts • Use high bandwidth communications (expensive)
Man-in-the-Middle (delay/loss, manipulation, theft)
synchsec  |  Origination, accuracy, availability, consistency |  Mismatch between obtained and expected value, abnormal delay in packet arrival |  • Mutual authentication, message authentication codes • Digital certificates with active management of CRLs
False Data Injection (manipulation, theft)
fdi1 ; fdi3 ; fdi4 ; fdi5 ; fdi6  |  Plausibility, consistency, accuracy, origination |  Mismatch with SCADA values, unexpected values, spatio-temporal outliers |  • Spatio-temporal correlations, density-based local outlier factoring • Monitor line impedance for anomalies • Random time-hopping of packets • Divergence and miscorrelation between SCADA and PMU data
Snooping, Sniffing (theft)
synchsec ; beasley  |  Plausibility, origin |  No observable changes- additional analysis needed |  • Using secure gateway/VPN communication • Employing TLS/SSL, SSH, lightweight selective encryption
Delay (delay/loss)
synchsec ; beasley  |  Completeness, consistency, availability, accuracy |  Observable patterns in gaps, slow arrival of packets |  • Statistical and intelligent substitutions • Redundant measurement devices on the same line
APT, Insider threat (delay/loss, theft, manipulation) human  |  Accuracy, consistency, origin, plausibility |  No observable changes- additional analysis needed |  • Defense-in-depth • Machine learning, advanced data analytics

Final conclusions can be gathered from eval7 . The report by the Pacific
Northwest National Laboratory (PNNL) analyzes existing synchrophasor networks
in terms of their communication and information-level interoperability,
security and performance. It concluded that latency is a key issue for the
future synchrophasor designs which is expected to compound latency due to PDC
functionality. It also emphasized that substations generally did not employ
redundancy; there is little consistency in adoption of security methods for
synchrophasor networks. Some tools include link-level encryption, virtual
private networks (VPNs), ID/IPS, firewalls and access control lists (ACLs).
Further, existing data quality checking methods locate a compromise in
integrity by identifying faulted data values (due to measurement errors,
communication delays or external events) but not due to result of device
tampering, MITM, spoofing or FDI. Since both faults and attacks have the same
impact on quality, it is important to differentiate the two causes while
checking for the attributes such as accuracy, consistency and timeliness.

To summarize, the following measures can be used as metrics to quantify data
quality: TVE, errors in magnitude, phase, frequency and ROCOF, harmonics and
noise for measurement accuracy; comparison between measured and expected
results, confidence interval and precision for measurement specifiers;
temporal, geospatial and topological accuracy for attribute accuracy; device
model specifications, geospatial and topological coordinates, coverage and
content for origination; persistence in Header and Data frames, standards
compliance, reporting rate and order for logical consistency; and gap rate,
gap size and largest known gap for completeness. Benchmarking and
standardization are two methods that can be used to evaluate data quality.
Similarly, cybersecurity can be quantified by conducting extensive penetration
testing of the synchrophasor networks integrated into benchmarked IEEE bus
systems for different types of attacks (DoS, MITM, FDI, spoofing, probing,
cache poisoning) and discovering potential vulnerabilities that could be
exploited. While doing so, it would be important to also repeat the evaluation
of the quality attributes using the above metrics and explore how they are
impacted due to the specific attacks, and whether they violate the industry
standards requirements specified for different applications.

###  4.2 Addressing Cyber-attacks Using Quality Issues

It can be seen from Table 7 that successful cyberattacks compromise
synchrophasor data quality since the security requirements are violated
cybersecreq . Given synchrophasors use TCP/UDP on the transport layer for
their communications, attacks typically possible on TCP/IP stack like DoS,
MITM, packet replay or spoofing are possible in synchrophasor domains as well.

Physical attacks like device tampering causes loss or incurs theft of critical
information, easily observed through large gaps sizes, poor accuracy in
obtained values and unreliable origin. The lost data is typically handled
through substitution, either statistical or intelligent exploringchallenges ;
synchsec . The best way to prevent physical attacks like cable disconnects,
direct damage to device, etc. is by ensuring the devices are isolated from
external weather and human elements.

Spoofing synchrophasor data is achievable through polynomial fitting or data
mirroring techniques. Such attacks impact quality that manifests as outliers
or noise. Several methods have been proposed to counter these attacks: intra-
PMU and inter-PMU correlations to determine the relationship between PMU
parameters and across PMUs in a locality, respectively; machine learning
techniques like Support Vector Machines (SVMs) and more fdi2 ; dataspoof ;
dataspoof2 .

GPS spoofing exploits publicly available civilian GPS signals using air or
cable to produce signals that initially align with the original, but slowly
start increasing the power to drown the authentic signal and thereby
compromising the receiver gpsspoof ; eval3 . By introducing measurement errors
in the time synchronization, the attacks induce changes in data consistency
and plausibility which can be used as markers to identify the likelihood of
the attack gpsspoofing1 ; gpsspoofing2 ; yuder .

In a successful DoS where multiple synchrophasor devices get compromised,
packet delay or loss is observed. This impact in quality can serve a clue to
the onset of DoS-style attacks. Typical solutions involve augmenting inline
blocking tools, high bandwidth connections, disabling IP broadcasts and port
hardening.

MITM is possible in synchrophasors where the attacker acts as a legitimate PDC
to the PMUs and vice-versa, thereby intercepting and/or modifying all messages
exchanged. This is noticed by quality checking methods in the form of poor
accuracy and consistency in values between what was sent by PMU and what was
received by PDC. It can be avoided by having the devices employ mutual
authentication and a digital certificate mechanism with an actively managed
Certificate Revocation Lists (CRLs) and certificate authorities synchsec ;
beasley .

FDI impacts the consistency, accuracy and plausibility of the data. The
effects are typically observed as spatio-temporal outliers in the data.
Quality checking methods check for this anomaly and may employ correlation
across different timestamps to identify the corruption of data. FDI is one of
the widely explored attacks on synchrophasor domain, with solutions like
determining the mismatch between the values obtained from PMUs and that
observed in SCADA, monitoring the line impedances which get affected when data
is manipulated, and using density-based Local Outlier Filter (LOF) analysis
fdi1 ; fdi3 ; fdi4 ; fdi5 ; fdi6 .

Sometimes, attackers simply capture the packets flowing in a channel with an
intent to listen. Such sniffing/snooping attacks have been conducted using
WireShark to realize messages are exchanged in plaintext. This attack is
difficult to detect using data quality checking methods since most often, no
quality characteristic is impacted as the attackers do not affect the data
actively. However, technologies like VPN, encryption of selective messages (to
reduce the overall process overhead), or Transport Layer Security (TLS)/Secure
Socket Layer (SSL), Secure Shell (SSH) can be used to mitigate them. While TLS
has been shown to be susceptible to poisoning attacks and VPN to side channel
attacks, careful network design can account for them synchsec ; snooping .

With the increased frequency of campaign efforts and nation-sponsored attacks
against the grid, synchro-phasors could be lucrative targets for sophisticated
attacks like advanced persistent threats (APTs), social engineering, watering-
hole attacks and malware-based intrusions tong ; flau ; lanchao ; jzhang ;
hastings . While these attacks scale beyond specific devices in the
synchrophasor hierarchy, the quality checking methods alone would not be
sufficient human . The use of defense-in-depth model augmented with
stakeholder interactions, awareness and training, and intelligent solutions
like machine learning for attack data classification and/or event prediction,
root-cause analysis of observed events, developing evolving defense
topographies using moving target defense, and advanced visualization
techniques for efficient cognition of events would play a critical role.

The key takeaway from this section is that impacts on data quality can provide
strong markers for an underlying cyber-attack. Noise, outliers and missing
values are all commonly observed issues which quality checking methods may be
programmed to detect, analyze and base decisions on. Certain sophisticated
attacks like APTs, insider threats, sniffing, and social engineering have
indirect impacts on quality which a checking method may not be able to detect
with enough confidence or precision. Additional solutions are required to
mitigate such attacks in the synchrophasor domain. These solutions include
statistical methods like divergence, correlation, regression and substitution;
intelligent methods like neural networks and evolutionary algorithms for event
classification and prediction, logistic regression for substitution;
technologies like VPNs, firewalls, ID/IPS, anomaly detectors, selective
encryption, port hardening, network isolation and use of TLS/SSL, SSH; and
human-in-the-loop solutions like advanced visualization techniques, awareness
and training, and stakeholder engagements. While the impacts on quality can
also be due to underlying device or measurement errors, most of the works in
the literature assume the data has been subject to delay/loss, manipulation or
theft intentionally. This paves way for the recommendation that the upcoming
research in this area must look at ways to differentiate the impacts on data
quality due to attacks from errors.

##  5 Future Directions of Research and Conclusion

The future directions of research in the areas of synchrophasor data quality,
cybersecurity and communications are multi-faceted. Addressing data quality
challenges must begin with a strong push to the adoption of industry-wide,
vendor-agnostic data management, processing and storage standards for smart
grid. Most recent cyber-attacks were successful due to the difference in speed
of cognition of the information generated by automated vulnerability detection
tools and the speed with which the machine data is created (called cognitive
gap) human . The design of synchrophasor devices are also expected to improve
in the future smartcity . Keeping in mind the quality challenges, an
improvement to PDC design called flexible integrated synchrophasor system
(FIPS) was proposed to minimize issues in quality and communication, and
tackles specific tasks of PDC such as data alignment, employs cryptographic
methods to ensure confidential exchange of data without jeopardizing
integrity, and establishes relevance to the NASPInet flexpdc . To ensure
device and application-level interoperability, development of technical
standards and conformance testing rules is expected. Further, the emergence of
distribution-level $\mu-$PMUs will evoke the need for developing measurement,
communication, quality and security standards. Further, with the deployment of
distributed renewable sources, electric and autonomous vehicles, energy
storage and transactive energy, there is a strong impetus for enhancing
technologies behind monitoring and control, of which synchrophasors will play
a major role eval2 .

To conclude, while existing research has focused on the synchrophasor
challenges of quality and cybersecurity individually, their interdependency
has largely been ignored. This paper makes one of the first attempts at
highlighting the impacts of cyber-attacks on various quality attributes,
thereby recommending that the future research on the design and development of
security solutions should account for their impacts on quality as well, and
that different quality characteristics can be used by quality checking methods
to flag for potential cyber-attacks. Plausibility, completeness, accuracy and
consistency are some of the attributes that are most adversely impacted by a
majority of the attacks on synchrophasors. At the same time, not all cases of
poor data quality imply a successful cyber-attack as the reason. Different
metrics that could be used to quantify quality attributes were summarized, and
the methods that help evaluate the impacts of quality and security on
performance were also briefly highlighted. This paper serves as a starting
point for researchers entering these areas as it summarizes and determines
their interdependency and relevance to smart grid security.

## Acknowledgments

This material is based on work supported by the National Science Foundation
Grant No. CNS-1553494 and the Department of Energy Grant No. 800006104. Any
opinions, findings, and conclusions or recommendations expressed in this
material are those of the authors and do not necessarily reflect the views of
the NSF and DOE.

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