Security and Privacy in Smart Grids: Challenges, Current Solutions and

Future Opportunities

Ismail Butun

, Alexios Lekidis

and Daniel Ricardo dos Santos

Chalmers University of Technology, Sweden

Eindhoven University of Technology, The Netherlands

Forescout Technologies, U.S.A.

Keywords:

Smart Grid, Cybersecurity, Network Intrusion Detection, Advanced Metering Infrastructure.

Abstract:

Smart grids are a promising upgrade to legacy power grids due to enhanced cooperation of involved parties,

such as consumers and utility providers. These newer grids improve the efﬁciency of electricity generation and

distribution by leveraging communication networks to exchange information between those different parties.

However, the increased connection and communication also expose the control networks of the power grid

to the possibility of cyber-attacks. Therefore, research on cybersecurity for smart grids is crucial to ensure

the safe operation of the power grid and to protect the privacy of consumers. In this paper, we investigate

the security and privacy challenges of the smart grid; present current solutions to these challenges, especially

in the light of intrusion detection systems; and discuss how future grids will create new opportunities for

cybersecurity.

1 INTRODUCTION

Smart grids are electric power systems that provide

automation, remote sensing, and remote control ca-

pabilities. They are a promising upgrade to legacy

power grids due to enhanced cooperation of involved

parties, such as consumers, utility providers, and dis-

tributed generators (Abdallah and Shen, 2018).

Smart grids improve electricity generation and

distribution through optimization and projection of

electricity consumption by leveraging communication

networks to exchange information between those dif-

ferent parties. However, increased connection and

communication also expose the control networks of

the power grid to the possibility of cyber-attacks. At

the same time, cyber-attacks on industrial networks

are becoming more frequent and more critical. There-

fore, research on cybersecurity for smart grids is cru-

cial to ensure the safe operation of the power grid as

well as to protect the privacy of consumers.

In this paper, we investigate the security and pri-

vacy challenges of the smart grid; present current so-

lutions to these challenges, especially in the light of

Intrusion Detection Systems (IDS); and discuss how

future grids will create new opportunities for cyberse-

curity.

In particular, the following are the main contri-

butions of this paper. In Section 2, we present the

key points of smart grids: network architectures, use

cases, and network communication protocols. In Sec-

tion 3, we describe security and privacy issues in the

smart grid. In Section 4, we discuss secure protocols,

IDS, and privacy regulations as current solutions to

the security and privacy challenges. In Section 5, we

consider new opportunities for cybersecurity in future

grids.

2 SMART GRIDS AND AMI

A smart grid consists of four segments: Generation,

Transmission, Distribution, and Consumption. Each

of these segments, especially the ﬁrst three, relies on

complex control signaling which is explained below.

The Generation control signals consist of 3

branches. (1) Automatic Voltage Regulator, where

generator exciter control is used to improve power

system stability by controlling the amount of reac-

tive power being absorbed or injected into the system.

(2) Governor Control is the primary frequency control

mechanism that detects disturbances and accordingly

alters settings to change the power output from a gen-

erator. (3) Automatic Generation Control (AGC) is a

Butun, I., Lekidis, A. and Santos, D.

Security and Privacy in Smart Grids: Challenges, Current Solutions and Future Opportunities.

DOI: 10.5220/0009187307330741

In Proceedings of the 6th International Conference on Information Systems Security and Privacy (ICISSP 2020), pages 733-741

ISBN: 978-989-758-399-5; ISSN: 2184-4356

733

Residential Home

Water

Gas

Heat

HAN

gateway

Utility Data Control Center

Smart

Metering

Operating

Suite

Aggregated

SCADA

Utility Access Point

Medium Voltage

IED or PLC

(Data

concentrator)

Data

analytics

Router -

Switch

Utility Access Point

High Voltage

Router -

Switch

RTU

IED or PLC

(Data

concentrator)

DLMS/COSEM

M-Bus

Wireless

Protocols

(ZigBee, KNX,

Wireless M-Bus,

etc.)

Legend:

Figure 1: AMI architecture.

secondary frequency control loop that ﬁne tunes the

system frequency to its nominal value.

The Transmission control signals consist of 2

branches. (1) State Estimation estimates system vari-

ables, such as voltage, magnitude, and phase angle,

by projecting faulty measurements from ﬁeld devices.

(2) Volt-Ampere Reactive (VAR) Compensation con-

trols reactive power injection or absorption to im-

prove the performance of transmission.

The Distribution control signals consist of 2

branches. (1) Load Shedding helps in preventing a

system collapse during emergency operating condi-

tions. These systems can be manual or use auto-

matic relays. (2) the Advanced Metering Infrastruc-

ture (AMI) is responsible for collecting, measuring,

and analyzing electric energy consumption data, as

well as transmitting this data to a central collection

facility. In a smart grid, the electricity usage of con-

sumers is measured by an enhanced metering device

(called a smart meter) that can transmit its measure-

ment data to the operators via a network connection.

2.1 Architecture

Figure 1 presents the architecture of a smart grid, fo-

cusing on the AMI and its networking protocols.

In the Residential Home, a Home Area Network

(HAN) connects smart meters and end-user applica-

tions in the same building to a local data collector

and gateway between access and local network. This

segment has a HAN gateway where the data of smart

meters and other smart devices is centrally stored to

be forwarded to a Data Concentrator. The HAN is

optional, since many devices can directly connect to

substations of the energy distribution company. In

this segment, we can ﬁnd the DLMS/COSEM (DLMS

UA, 2019) protocol for conﬁguring, reading infor-

mation from, and writing information to smart me-

ters. Many European meters also use M-Bus (EU,

2019) for meter reading, which is compatible with

the DLMS/COSEM application layer and includes a

radio-assisted extension, called Wireless M-Bus, to

transmit meter data over GSM/GPRS interfaces.

The Utility Access Point of Medium Voltage repre-

sents a substation of the energy distribution company.

It contains Intelligent Electronic Devices (IEDs) or

Programmable Logic Controllers (PLCs), which are

industrial computers that enable advanced power au-

tomation. These devices receive smart meter informa-

tion via a wireless connection and serve as data con-

centrators by aggregating the data of multiple smart

meters and storing them in dedicated databases. They

then calculate actual energy consumption, based on

the meter readings, and compare it to an estimated

consumption associated with historical data for each

house and climate conditions in the neighborhood.

Both the actual and the estimated energy consump-

tion are then forwarded to the next part of the AMI by

a dedicated Router. These substations can also have

a Data Analytics module to provide consumption re-

ports to the operators.

The Utility Access Point of High Voltage also

contains IEDs, PLCs and Routers to forward aggre-

gated data to a centralized location of the electricity

provider, called Utility Data Control Center.

The Utility Data Control Center is the manage-

ment system of the energy distribution company,

which has an overview of aggregated data from

each consumer, as well as the center for utility and

customer-related services through the Smart Metering

Operating Suite. In this center, we can also ﬁnd the

Supervisory Control and Data Acquisition (SCADA)

system that receives all the required analytics from the

dedicated components of the Utility Access Point of

Medium Voltage. The communication with the pre-

vious three segments was initially via the Power Line

Communication (PLC) protocol (Galli et al., 2011),

which relies on existing power lines to transmit data

signals. But as the amount of data transferred in-

creased exponentially over the last years, the PLC

medium was found to be difﬁcult and noisy, thus

adding unpredictable delays to transmissions and dis-

ICISSP 2020 - 6th International Conference on Information Systems Security and Privacy

734

Figure 2: Distribution use case.

turbances. Additionally, in residential neighborhoods

the average bandwidth is very low. Hence, wireless

or cellular technologies started to be used in the com-

munication with the Utility Access Point of Medium

Voltage, especially ZigBee, KNX, and Wireless M-

bus (Mahmood et al., 2015).

2.2 Use Cases

The AMI is the main enabler for the smart grid. Thus,

we present below two use cases derived from (but not

limited to) it.

Distribution. Smart meters can quickly notify elec-

tricity distributors if the power is out in a certain area,

thus enabling early-stage fault identiﬁcation and lo-

cation. Problems can be located faster, repair crews

can be prioritized, and repairs can begin sooner. Cus-

tomers can also receive information about outages in

their area and estimated repair times.

Figure 2 depicts the distribution use case as fol-

lows. Power plants generate energy, which is trans-

ferred through poles and wires to substations. Sub-

stations use their transformers to reduce the voltage

and distribute energy to neighborhoods. They also

use data concentrators to estimate and forecast energy

consumption based on historical data and environ-

mental conditions. Finally, to perform more accurate

estimations, concentrators may initiate read requests

to the smart meters. Each step of the use case uses dif-

ferent protocols, shown in green (for application layer

protocols) and red (for lower-layer protocols) in the

Figure. The PLC and DLMS/COSEM protocols were

presented above. LTE, WiFi, GPRS, and LoWPAN

are lower-layer wireless protocols that are not the fo-

cus of our analysis; the interested reader is referred to

(Mahmood et al., 2015) for details. DNP3, GOOSE,

and MMS are application-layer protocols that will be

described in details in Section 2.3 (GOOSE and MMS

are mappings of the abstract data model deﬁned in

the IEC 61850 standard, which is detailed in that Sec-

tion).

Billing. Billing is a use-case that directly leverages

the collection of consumption proﬁles at customer

Figure 3: Billing use case.

households and the tariff data from the respective cus-

tomer’s contract to calculate a ﬁnal electricity bill,

thus allowing ﬂexible pricing plans where the cost of

electricity changes according to when it is used.

This use case is shown in Figure 3. Smart meters

periodically record the consumption proﬁle at the cus-

tomer’s household, which is transmitted via network

packets to the data concentrators that are usually lo-

cated in substations. Concentrators use the poles and

wires to transmit the consumption proﬁle to the util-

ity control center. Energy providers use the consump-

tion proﬁle and their tariff rates to calculate the ac-

tual energy price that the customer must pay for the

time period covered by the proﬁle. Finally, the energy

price along with energy analytics is made available to

the consumer, who can compare it with statistics from

other consumers and use it to manage more efﬁciently

its energy consumption. As before, the communica-

tion protocols shown in the Figure will be explained in

Section 2.3, with the exception of MQTT and CoAP,

which are IoT protocols for data exchange between

resource-constrained devices (Naik, 2017).

2.3 Communication Protocols

The control signals that enable the use cases discussed

above are communicated in a smart grid via network

protocols that have already been cited. In this Section,

we describe in details the most important application-

layer protocols used in a smart grid.

DLMS/COSEM. DLMS/COSEM (DLMS UA,

2019) is the de-facto standard for reading and

conﬁguring smart meters in Europe. The protocol

is based on a common data model and application

layer used over different communication media.

DLMS/COSEM is a client-server protocol where the

server is a meter and the client can be a gateway or

central ofﬁce. The standard speciﬁes the data model

and commands to control smart meters. The COSEM

object model speciﬁes the smart metering functions

in different applications, e.g., Data storage, Access

control and management, Time and event bound

control, and Payment metering.

Security and Privacy in Smart Grids: Challenges, Current Solutions and Future Opportunities

735

The DLMS/COSEM standard provides two secu-

rity mechanisms: authentication (also called secu-

rity level) and encryption (also called security suite).

The two mechanisms often use the same keys, but

they can be chosen independently of each other and

can be used in any combination. The following au-

thentication mechanisms exist: Lowest Level Secu-

rity (0), where no authentication is used; Low Level

Security (1), where only the client is authenticated

using a plain text password; and High Level Secu-

rity (> 1), where both client and server are authenti-

cated. The cryptographic algorithm used for authen-

tication depends on the HLS level (e.g., level 2 is for

vendor-speciﬁc algorithm, whereas levels 3-7 are for

MD5, SHA-1, GMAC, SHA-256, and ECDSA, re-

spectively). The encryption mechanism is used to en-

crypt messages and add authentication tags to individ-

ual messages. The commonly implemented encryp-

tion mechanism in DLMS/COSEM is based on AES-

GCM-128. It uses the global unicast encryption key

and, if available, the authentication key. Optionally,

a client may send a so-called dedicated session key

to the server during connection setup. The dedicated

key is then used instead of the global encryption key

for the remaining communication of this connection.

The dedicated key is a temporary key that is usually

generated ad-hoc at connection time.

M-Bus. M-Bus is based on the European standard

EN 13757-2 (EU, 2019) for the remote reading of gas,

electricity, and other meters. M-Bus is a binary pro-

tocol, where the commands are contained in so-called

M-Bus telegrams. The protocol is based on a mas-

ter/slave communication model and can be operated

as a line, star or tree topology. The master powers the

serial bus and processes the data of the M-Bus slaves

(measurement devices). The main beneﬁts of M-Bus

are: a single bus cable connects all meters to a central

system; bus nodes are supplied directly via the two-

wire bus; and devices from different manufacturers

can be connected to a bus system.

M-Bus does not deﬁne any transport or network

layer protocol, but instead uses the application layer

to deﬁne the messages that are exchanged in the

Master-Slave architectural model. The architectural

model can be either based on the EN 13757-3 (EU,

2019) standard or the DLMS/COSEM application

layer. There are four types of messages in the data

link layer and, depending on their type, some ﬁelds

are vendor-speciﬁc.

M-Bus offers password authentication before sen-

sitive commands are executed and EN 13575-4 (EU,

2019) introduced the use of AES encryption. A com-

prehensive security analysis of M-Bus was conducted

in (Brunschwiler, 2013).

Figure 4: AMI control functions (Sridhar et al., 2011).

DNP3. Distributed Network Protocol-3 (DNP3)

(IEEE, 2010) is a set of communication protocols

used in process automation systems, especially util-

ity distribution, such as electricity and water. The

protocol was developed for communications between

various types of data acquisition and control equip-

ment, such as SCADA control centers, Remote Ter-

minal Units (RTUs), and IEDs. Competing standards

include the newer IEC 61850 protocol, discussed be-

low.

IEC 61850. IEC 61850 is a standard deﬁning com-

munication protocols for IEDs at electrical substa-

tions, which is a part of the IEC TC 57 reference ar-

chitecture for electric power systems. The abstract

data models deﬁned in IEC 61850 can be mapped to

a number of protocols (TC57, 2019). IEC 61850 was

intended to replace DNP3 in substation communica-

tions. However, current IEC 61850 is only limited

within a power substation. It is projected that IEC

61850 would be used for outside substation commu-

nications as well in the near future (Wang and Lu,

2013).

3 CHALLENGES

In cyber-physical systems, such as smart grids, the

goal of cybersecurity is to protect the conﬁdentiality,

integrity, and availability of systems, information, and

related assets, with privacy usually mentioned as an

additional desirable property (Butun, 2017). In this

Section, we discuss the challenges of protecting the

security and privacy of smart grids.

3.1 Security

The security of an AMI is fundamental in the deploy-

ment of smart grids (Faisal et al., 2014). Figure 4

depicts AMI control functions that can halt the opera-

tion of a single smart meter or the whole network, de-

pending on the source and type of the control signal.

Therefore, these control signals should be secured to

prevent malicious manipulation. Smart meters have

three main types of interfaces on which attacks can

be launched: optical, wireless, and cellular. Below,

ICISSP 2020 - 6th International Conference on Information Systems Security and Privacy

736

we describe potential attack scenarios on each inter-

face.

Attacks via the Optical Interface. A smart me-

ter’s optical interface can be connected to a laptop

through a dedicated cable connected to a dedicated

port, which varies across different countries. An at-

tacker can use specialized tools to analyze or set the

values of a connected smart meter. By connecting

a physical device (e.g., Raspberry Pi), a malicious

actor can launch attacks to control different parts of

the Residential Home. These attacks are feasible as

a physical connection to the meter directly bypasses

encryption. However, such attacks require physical

access of a malicious actor to the residential home.

Attacks via Wireless Networks. A smart meter’s

wireless interface is used for periodic data collec-

tion to calculate daily energy consumption from each

HAN. The periodic nature of data collection makes

the packet ﬂow on the network predictable. Thus,

malicious actors can tamper with the normal behavior

by, for instance, blocking the periodic transmissions.

Wireless attacks can have an effect in the Residential

Home, where a malicious actor needs to be in proxim-

ity (i.e. positioned in the neighborhood between the

meter and the Utility Access Point). A potential sce-

nario leveraging ZigBee is as follows. First, use radio

jamming (Algin et al., 2017) over the ZigBee frequen-

cies to jam the communication between the meter and

the Utility Access Point and wait for the users to re-

pair the device. Then, eavesdrop the communication

and navigate to the DLMS/COSEM part of a message

(Kistler et al., 2009). From there, the attacker can,

e.g., eavesdrop requests and send responses that dis-

able power connection. Wireless attacks mostly affect

the Residential Home or the Utility Access Point seg-

ments and they can have consequences on electricity,

hardware, and data loss or leakage for the customers.

Attacks via Cellular Networks. Cellular attacks can

be launched when having remote access to the Utility

Access Point of Medium/High Voltage. In this sce-

nario, a malicious actor needs to eavesdrop the GSM

communication between the Utility Access Point and

the Utility Data Control Center. This can happen as

follows. First, the attacker can use a radio sniffer to

listen to GSM trafﬁc. If the data on this trafﬁc is en-

crypted, the key can be recovered using the Barkan-

Biham-Keller attack scheme (Barkan et al., 2008).

Then, the attacker can connect to the RTU at the sub-

station gateway and ﬁnally open the circuit breakers

of the substation to stop power transmission. Alter-

natively, a malicious actor could rely on physical ac-

cess of a utility access point. The utility access points

may be accessed only occasionally by the utility op-

erators and this increases their insecurity by making

Figure 5: Smart meter ports that generate data.

them vulnerable to malicious physical access, which

would allow an attacker to connect to the RTU di-

rectly. These attacks target the Utility Access Point of

Medium/High Voltage segment, but they have devas-

tating economic consequences for the utility company

as well as may cause data loss for its customers.

Other Attacks. Traditional attack classes that can

affect smart grids include Denial of Service, Replay,

Brute forcing, Radio Jamming, and Identity Spooﬁng.

The possible consequences of such attacks include:

power outage, data leakage, bricked devices, loss of

trust from customers and ﬁnancial loss for utilities.

3.2 Privacy

Metering data can be privacy-sensitive, such as en-

ergy usage readings, or non-sensitive, such as volt-

age quality data and information about the meter it-

self. Utilities must protect the privacy of consumers

by safekeeping this data. However, in the smart grid,

consumers may have to share their data with their util-

ities, which in turn might share them with other enti-

ties that want to use them, such as advisory or insur-

ance companies. This leads to data leakage issues.

Below, we explain how data is shared in smart meters

and how other data can be inferred.

Data from Smart Meters. Metering data can be

accessed through four smart meter ports (P0-P3), as

shown in Figure 5. P0 is used for local connection

during installation and maintenance work. P1, also

called the consumer port, allows for communication

with third party equipment locally installed at the con-

sumer’s house. The port only supports communica-

tion from the meter to this equipment, not the other

way around. Via P1, the meter provides real-time

measurements in periodic intervals and it can be used

to display messages on the connected equipment. P2

connects to other local metering equipment, e.g., wa-

ter and gas meter. This port can be wired or wireless.

Water and gas meters send their measurements to the

electricity meter periodically and afterwards it can re-

main with the electricity meter or be forwarded. P3

Security and Privacy in Smart Grids: Challenges, Current Solutions and Future Opportunities

737

communicates with the utility company to send me-

ter readings, status checks, power quality and outage

measurements, and remote updates. Unlike P1, P3

supports two-way communication. P3 energy data are

not available in real-time. There is an additional P4

port that allows the utility company to provide meter-

ing information to third parties.

Data Inference. Using the data transmitted by smart

meters, there are two methods to infer more user data.

The ﬁrst method, called Non-Intrusive Load Mon-

itoring (NILM) (Lisovich et al., 2010), allows to in-

fer information such as location and behavior of users

(e.g., if they are at home), the amount of energy they

consume, and the type of devices they own. NILM

separates the energy data into categories, such as heat-

ing, appliances, entertainment, lighting, hot water,

and cooking. Based on these categories, the utility

company can use disaggregation techniques to infer

details on the energy ﬁngerprint of each appliance.

Anyone who gets hold of this data gets a glimpse of

what appliances are used and how often they are used.

This allows to get details on the electricity consump-

tion of an individual household or an entire neighbor-

hood. NILM is successful in the HAN and small busi-

nesses because of the low event generation rate and

number of loads at these sites. Larger commercial

and industrial facilities require a more sophisticated

approach, due in part to high rates of event genera-

tion, load balancing, and power factor correction.

The second method relies on the real-time mon-

itoring capabilities of energy consumption proﬁles

from smartphone applications. This method can be

used to infer total energy consumption data. Based

on the total energy consumption data, an adversary

may infer presence information i.e. if the consumer is

at home. Nevertheless, it is difﬁcult to derive energy

consumption proﬁles for the house appliances, as this

information is available from the utility company.

4 CURRENT SOLUTIONS

Cybersecurity systems should be layered and com-

bine Prevention, Detection and Mitigation (Butun and

Osterberg, 2019). This Section discusses current so-

lutions to the challenges presented in Section 3, espe-

cially in the form of secure communication protocols

(for Prevention), network monitoring (for Detection)

and privacy regulations (for Mitigation).

4.1 Secure Communication Protocols

Secure protocols are crucial to avoid remote attacks

in smart grids. DNP3 and IEC 61850, presented in

Section 2.3, did not have inherent security from the

beginning. Therefore, Secure DNP3 and Secure IEC

61850 (known as IEC 62351) are proposed to achieve

end-to-end security for smart grid communications by

adding an extra layer in the protocol stacks called

“Encryption and Authentication” in between the Ap-

plication and Network layers. As discussed in Sec-

tion 2.3, DLMS has deﬁned a data protection secu-

rity layer that provides encryption and authentication

mechanisms.

4.2 Network Monitoring

Network monitoring should be in place to detect com-

plex attacks. IDSs implement network monitoring

and they can be classiﬁed into three categories ac-

cording to their detection methodology: misuse-based

(also called signature-based), speciﬁcation-based, and

anomaly-based. Signature-based detection is difﬁ-

cult to apply to smart grids, since their ever-growing

threat surface requires a constant rule-set update.

Speciﬁcation-based IDS is also challenging due to

the difﬁculty of deriving speciﬁcations for the dy-

namically changing smart grid architectures. Finally,

anomaly-based IDS can, in principle, detect any kind

of bad (or anomalous) behavior by using either data-

oriented or behavior-oriented (Kwon et al., 2015)

mechanisms, tailored to the communication protocols

of Section 2.3.

Architecture and Deployment. To detect cyber-

attacks effectively, it is important to know where and

how to deploy network monitoring solutions on a

smart grid. Below, we present three possibilities for

deployment, using as a framework the architecture de-

scribed in Figure 1. For each deployment option, we

describe the placement of IDS components, their ad-

vantages and disadvantages.

Before describing the deployment of network

monitoring solutions, we must deﬁne their compo-

nents. Practical intrusion detection systems have at

least two components: a Monitoring Sensor and a

Command Center. The Monitoring Sensor is respon-

sible for snifﬁng the network trafﬁc (usually pas-

sively, without injecting any trafﬁc, to avoid disrupt-

ing the network or delaying other packets) and ei-

ther forwarding raw trafﬁc or events (such as secu-

rity alerts and operational anomalies) to a Command

Center. The Command Center acts as a user inter-

face with which a security analyst can interact. It

also allows to connect multiple sensors, thus retriev-

ing trafﬁc or events from multiple locations (e.g., sub-

stations). This kind of architecture is followed by

both commercial and open-source IDS.

It is also important to notice that modern com-

ICISSP 2020 - 6th International Conference on Information Systems Security and Privacy

738

mercially available network monitoring solutions for

smart grids provide much more than just intrusion de-

tection. These solutions usually embed asset visibil-

ity and management options, which allow network

operators to see important information about all the

devices in the network, such as hardware and soft-

ware versions, protocols supported, and the presence

of vulnerabilities. Additionally, intrusion detection

in this domain has been extended with operational

anomaly detection, which allows network operators

to see abnormal or dangerous events that are not nec-

essarily related to a security incident but may be in-

dicative of a device failure. Other use cases include

network trafﬁc forensics (Corey et al., 2002), inte-

gration with Security Information and Event Manage-

ment (SIEM) solutions (Bhatt et al., 2014), and sup-

port for network segmentation (Genge and Siaterlis,

2012).

Residential Home. The ﬁrst deployment option

places the monitoring sensor at the Residential Home,

relying on the HAN gateway. The IP forwarding func-

tionality of the HAN gateway can be leveraged by

the monitoring sensor to monitor all the communi-

cation in the home network, which includes trafﬁc

from smart meters and other devices that have wire-

less interfaces. This deployment option allows to de-

tect security and operational anomalies in the smart

meters and devices in the home. The failures are re-

ported through wireless event forwarding to a Com-

mand Center that is placed in the Utility Data Con-

trol Center. The main advantages of this deployment

are full communication visibility inside the HAN and

the detection of attacks targeting smart meters. The

main disadvantages are: (i) limited scalability due to

the high cost and effort for sensor conﬁguration and

maintenance; and (ii) no visibility of the Utility Ac-

cess Points or the Utility Data Control Center.

Utility Access Point. The second deployment op-

tion places the sensors at the Utility Access Points.

When data is directly sent by the smart meters to a

data concentrator (avoiding a HAN gateway), this is

the only possibility to capture data and detect anoma-

lies. This deployment has the following advantages:

(i) full communication visibility of the smart meter

communication, as well as network monitoring capa-

bilities to ensure the integrity of data analysis; and (ii)

detection of remote attacks for the smart meters. The

main disadvantages are: (i) there may be many Util-

ity Access Points, resulting in high deployment and

maintenance effort; and (ii) it cannot detect attacks

targeting the Utility Data Control Center.

Utility Data Control Center. The third deployment

option places the sensors at the Utility Data Control

Center when the number of Utility Access Point sta-

tions is sufﬁciently large. This deployment option has

as main advantages low cost and effort, since only one

sensor should be deployed to monitor the activities

happening on the SCADA and logs in the aggregated

database. The main disadvantage is that there is no

visibility for attacks targeting the smart meters or the

Utility Access Point.

Evaluation. The selection of deployment option must

be tailored to the needs of each electricity company,

depending, for instance, on the attacks it wants to de-

tect and the geographical area that it covers. Even

though state-of-the-art solutions in the academic lit-

erature usually use the ﬁrst deployment option (in

the Residential Home), we believe that this is neither

scalable nor maintainable for larger residential areas.

Moreover, it is disruptive to the end-user, who may

not accept or trust it as a standalone technology. In-

stead, a conﬁguration that is integrated to the HAN

gateway may be more trustworthy. Our conclusion for

the second and third deployment options is that they

can be used in different settings. For an electricity

company with a small number of Utility Access Point

stations the best option may be the second, since it

provides visibility and access on the entire network.

For large energy providers, a deployment in the Util-

ity Data Control Center (third option) is more suit-

able, as the second option will require substantial ef-

fort for maintenance.

4.3 Privacy Regulations

Encryption is common to protect the privacy of con-

sumer data in smart grids. However, real scenarios

have shown that utilities may use a shared key for

their meters and, once this key is inferred, an adver-

sary can have access to data from all the meters of

the same utility (Burton, 2016). When attacks affect-

ing the privacy of users happen, regulations are an ef-

fective way to ensure that users will be notiﬁed and

that companies will take measures to avoid future in-

cidents.

The General Data Protection Regulation (GDPR)

went into effect in 2018 and has been formulated to

protect the privacy of EU citizens. It requires online

services that collect data to inform users about their

data collection processes and obtain consent; and en-

sure that collected data is stored in a secure envi-

ronment and is available to third parties or enforce-

ment ofﬁcials in deﬁned time frames (Sharma, 2018).

Smart grid operators are affected by the GDPR since

they collect and process personal data and make it

available to other stakeholders. A speciﬁc data pro-

tection and security framework for smart grids has

been proposed in the Electricity Directive. The aim

Security and Privacy in Smart Grids: Challenges, Current Solutions and Future Opportunities

739

is to include relevant GDPR provisions in the new

text and tailor those to the needs of smart meters. It

follows that a new, comprehensive legal framework

to ensure a high level of personal data protection in

smart metering systems is being shaped, which is ex-

pected to lead to greater trust and conﬁdence of en-

ergy consumers and, in turn, to their increased accep-

tance and participation in the smart grid (Fratini and

Pizza, 2018).

5 FUTURE OPPORTUNITIES

Future smart grids are expected be different as the

mass generation and distribution of electricity will be

replaced by local renewable resources such as solar

and wind. Besides, some of the solutions discussed

above, such as encrypted communication protocols

and strict regulations are expected to become more

popular. Thus, this future scenario presents opportu-

nities in 3 areas that we would like to highlight:

Uniﬁed Security Solution. Future security solutions

should focus on identifying security incidents through

indicators of suspicious behavior. Such indicators

arise from monitoring the network as well as appli-

cation logs from smart grid supervisory systems such

as the Data Analytics or SCADA systems of Figure

1. Additionally, the presence of dedicated incident

scoping and investigation scenarios will aid in rea-

soning about the incident’s root cause and minimize

false positives. Upon investigation, incident response

actions shall be taken, which include next-generation

ﬁrewalls to prevent unauthorized communications as

well as containment and recovery actions for the in-

volved smart grid assets. The ﬁnal goal of security

solutions should be to maintain the continuous smart

grid operation.

Encryption. Encryption is an often advocated mea-

sure for data security and privacy, but it is not effective

against several classes of attacks in industrial control

systems, while it can severely decrease visibility and

monitorability of smart grid networks (Fauri et al.,

2017). As communication protocols for the smart grid

evolve, encryption is a common additional capability.

However, deciding what communications to encrypt

and when to encrypt them may be as important as de-

ciding how and where to monitor network trafﬁc in

order to obtain the best results for intrusion detection.

This creates an opportunity for the design of commu-

nication protocols that at the same time protect the

information being communicated and allow for the

monitoring of potentially malicious behavior.

Distributed Grids. The more a grid becomes dis-

tributed, the more its attack surface is spread across

its different parts. Monitoring a distributed grid re-

quires a different deployment of sensors than what we

presented above, since the threats are also different.

For instance, it could be possible for future malicious

actors to compromise the stability of the grid by at-

tacking several small generation units. That would re-

quire a more distributed presence of sensors (such as

the one described in our ﬁrst deployment option). A

similar attack scenario, but manipulating distributed

electricity demand, instead of generation, has already

been described in the literature (Soltan et al., 2018).

6 CONCLUSIONS

As traditional power grids become smart grids, crit-

ical systems are connected to the users and poten-

tially reachable from anywhere in the world. This

brings beneﬁts but also exposes a previously closed

network to potentially malicious outsiders. This work

presented security challenges, solutions and opportu-

nities for smart grids in a comprehensive way, includ-

ing deﬁnitions, architecture, use cases and networking

protocols.

ACKNOWLEDGMENTS

This research has been partially supported by the

Swedish Civil Contingencies Agency (MSB) through

the projects RICS, by the EU Horizon 2020 Frame-

work Programme under grant agreement 773717, and

by the STINT grant IB2019-8185.

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