An Information Security Model for an IoT-enabled Smart Grid

Abeer Akkad

1,2 a

, Gary Wills

and Abdolbaghi Rezazadeh

Electronic and Computer Science Dept., University of Southampton, University Road, Southampton, U.K.

Information Systems Dept., Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, K.S.A

Keywords: IoT, Internet of Things, IoT-enabled Smart Grid, IoT & Security, Cybersecurity, Threats Modelling.

Abstract: The evolution of an Internet of Things-enabled Smart Grid affords better automation, communication,

monitoring, and control of electricity consumption. It is now essential to supply and transmit the data required,

to achieve better sensing, more accurate control, wider information communication and sharing, and more

rational decision-making. However, the rapid growth in connected entities, accompanied by the increased

demand for electricity, has resulted in several challenges to be addressed. One of these is protecting energy

information exchange proactively, before an incident occurs. It is argued that Smart Grid systems were

designed without any regard for security, which is considered a serious omission, especially for data security,

energy information exchange, and the privacy of both the consumers and utility companies. This research is

motivated by the gap identified in the requirements and controls for maintaining cybersecurity in the bi-

directional data flow within the IoT-enabled Smart Grid. The initial stages of the research define and explore

the challenges and security requirements, through the literature and industrial standards. The Threat

Modelling identified nine internet-based threats. The analysis proposes a security model which includes 45

relevant security controls and 7 security requirements.

1 INTRODUCTION

The Smart Grid (SG) can be regarded as an extensive

Cyber-Physical System (CPS) (Dagle, 2012). It is

considered to be a critical infrastructure in all

communities worldwide. Globally, the energy market

is believed to be the most important asset that allows

a country to expand its economy (Bedi et al., 2018).

Moreover, as cities want to assure sustainable green

energy as a step towards their transformation into

smart cities, implementing a SG is considered the best

way to achieve this goal. Thus, the SG is one of the

largest applications of IoT (Reka and Dragicevic,

2018; Al-Turjman and Abujubbeh, 2019). The

McKinsey Global Institute predicted that the IoT will

have a significant economic contribution from $3.9 to

$11.1 trillion per year by 2025 (Manyika et al., 2015).

This influence will be felt in many areas and

applications, including homes, factories, retail

environments, offices, worksites, human health,

outside environments, cities, and vehicles (Dalipi and

Yayilgan, 2016).

https://orcid.org/0000-0002-7710-6378

https://orcid.org/0000-0001-5771-4088

https://orcid.org/0000-0002-0029-469X

The conventional power grid uses an analogue and

electromechanical infrastructure in which electricity

is transmitted from a centralised utility or power plant

to the consumer through long-distance and high-

voltage lines. The power is delivered to the

neighbourhood by a distribution system consisting of

transformers, distribution substations, and power

lines. In this unidirectional model, there is no

feedback from the consumer (Al Khuffash, 2018), so

utility companies depend on meter readings by

engineers to ensure that the balance of supply and

demand is met in an effective manner. Meter readings

provide insufficient information on the grid’s

condition and consumption, with no real-time energy

information (Al Khuffash, 2018). Consequently,

consumers are faced with being consumption-

conscious. Besides real-time challenges, there are

significant issues of exponential growth and changes

of demand, an outdated grid architecture, latency,

variations in load, many power outages, and increased

carbon emissions (Al Khuffash, 2018). New

infrastructure is needed that may overcome these

Akkad, A., Wills, G. and Rezazadeh, A.

An Information Security Model for an IoT-enabled Smart Grid.

DOI: 10.5220/0011042200003194

In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security (IoTBDS 2022), pages 157-165

ISBN: 978-989-758-564-7; ISSN: 2184-4976

157

challenges, and the evolution of a SG could handle

these drawbacks associated with the conventional

grid.

The electric utility sector is currently developing an

IoT-enabled SG. This is viewed as the largest-ever

installation of an IoT, with thousands of smart objects

and things such as smart meters, smart appliances,

and other sensors (Reka and Dragicevic, 2018). This

huge number of connected devices, besides the

increasing demand for electric energy, results in

significant challenges for a SG. Although the SG can

address the drawbacks of the traditional power

system, it involves issues of security, Big Data

processing, cost, centralisation, scalability,

interoperability, heterogeneity, and latency.

This research discusses the present challenges of

an IoT-enabled electricity Smart Grid, focusing on

securing the information flow that is essential for

better automation, sensing, controlling,

communicating, and timely decision-making (U.S.

Department of energy, 2018). The current research

proposes a comprehensive model for securing IoT-

enabled SG.

This paper is organised as follows: Section 2

defines the IoT-enabled SG and components

highlighting the security and the link between IoT and

SG. In section 3 the security requirements are

investigated. Section 4 looks at the threats modelling

and identifies the security threats and controls. Then,

the security model is proposed in section 5. Also, the

potential future work is briefly discussed.

2 BACKGROUND

This section of the paper offers an overview of IoT-

enabled SG, components, Then, the role of IoT in the

SG is explained highlighting the security of IoT-

enabled SG.

2.1 Definition of IoT-enabled Smart

Grid

The SG can be defined as the integration of ICT into

the existing electrical network, consisting of

renewable sources and involving its multiple domains

(generation, transmission, distribution, and

consumption) in the efficient automation and real-

time demand management of a reliable, sustainable,

bi-directional, and economic green electrical energy.

(IEEE, 2018; U.S. Department of energy, 2018;

EPRI, 2005).

2.1.1 What Makes the Grid Smart?

It is argued that digital technology is what makes the

grid smart (U.S. Department of energy, 2018). In

order to achieve this, information technology systems

have to be deployed to supply the data required for

better sensing, precise control, wider information

communication and sharing, powerful computing,

and better decision-making (U.S. Department of

energy, 2018).

2.2 Smart Grid Conceptual Model

The conceptual reference model by NIST (US

National Institute of Standards and Technology) is

commonly referred to in the sector (NIST, 2014).

However, the NIST model encounters a lack of detail

in terms of cybersecurity and information flow,

especially in the IoT infrastructure. The NIST model

contributes to the concept of the SG architecture only,

while this research fills in the gaps in the NIST model

to develop a case study that is useful for the related

sectors. NIST case studies and scenarios are limited

to privacy and some domains of SG without linking

security requirements, threats, and controls for each

access point in the system. Indeed, NIST IR and

NERC CIP measure the compliance of any

organisation with the policies.

2.3 IoT and Smart Grid

In this section, the role of IoT in the SG is explained.

Both Kaur and Kalra (2016) and Al-Ali and

Aburukba (2015) suggested that all objects in a SG

can be represented as IoT devices distributed

throughout the residential network, substations, and

utilities. These devices require tracking for

monitoring purposes, connectivity, and automation

(Al-Ali and Aburukba, 2015; Saleem et al., 2019).

The IoT is an enabling technology that brings internet

connectivity to the SG (Al-Ali and Aburukba, 2015;

Saleem et al., 2019). From the cyber-physical

systems point of view, SG is considered as one of the

biggest applications of IoT (Al-Turjman &

Abujubbeh, 2019; Reka & Dragicevic, 2018).

In SG, in the context of IoT each device is

connected to the internet. To facilitate communication

of information and receiving control commands via the

internet protocols, each must have a unique IP address

(Saleem et al., 2019). Under the IP addressing

schemas, IoT can offer monitoring and control capabi-

lities for SG, as discussed by Kaur and Kalra (2016).

This monitoring aspect can cover the generation plant,

distribution, storage, and finally consumption to

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

158

achieve efficiency management, demand management,

renewable energy needed measurement, and CO

emissions administration. Therefore, IoT devices

contribute to the reduction of wasted energy and the

accurate estimation of required energy.

Further, those devices exchange data in bi-

directional flow via the SG communication layer,

using several communication protocols, such as Wi-

Fi, Zigbee, WiMax, LET, and GPRS. IoT

standardises communication, reducing the number of

these protocols relating to the SG components (Al-Ali

and Aburukba, 2015). Both Saleem et al. (2019), and

Al-Ali and Aburukba (2015), emphasised that IoT

technologies enable SG to communicate across all its

multiple subsystems of generation, transmission,

distribution, and consumption. Al-Ali and Aburukba

(2015) stated that each device can exchange data and

commands from the control centres and utilities.

Mugunthan and Vijayakumar (2019) supported the

claim that IoT technologies have afforded SG with

the cloud, 5G, mobile wireless networks, application

programming interfaces (APIs), machine learning,

AI, predictive analytics, and Big Data management.

2.4 Smart Grid and Security

SG affords many opportunities, but it also presents

security challenges. To get the most out of SG, it is

essential to develop a highly secure information

system. it is argued that automation systems such as

SCADA were designed without any regard for

security (Aloul, 2012). Moreover, Modbus, which

exchanges SCADA information to control industrial

processes, was not intended for critical security

environments such as SG (Aloul, 2012). Thus,

securing the information system in SG must be

assigned the highest priority, since power assets

represent critical national infrastructure that may

attract terrorists and state actors. Any damage, such

as security attacks on the power grid, could cause

chaos across whole cities. Electric Power Research

Institute (ERPI) reported that one of the main

concerns in SG implementation worldwide is

security. Security challenges of IoT-enabled SG can

arise for many reasons. First, the entities in SG

communicate using the IP-based communication

network, exchanging sensitive and private data

between both consumers and utility companies. Such

networks are susceptible to many types of security

threat, such as man-in-the-middle, denial of service,

eavesdropping, and replay attacks, as shown in

section 3. Secondly, SG consists of various

components that communicate with one another,

which requires interaction among these technologies.

Accordingly, this communication introduces access

points in SG that are vulnerable to security attacks

(Mahmood et al., 2016). Thirdly, SG uses wireless

sensor networks to connect smart meters, for

example. It has been argued that wireless networks

are insecure (He et al., 2013). Fourthly, by allowing

unauthorised access to SG, the bi-directional

information flow may expose SG to many threats.

Fifthly, utilising IoT in SG may cause it to inherit

IoT’s security issues. For monitoring and control

purposes with IoT devices, SG should use the internet

(Ghasempour, 2019).

There are several security concerns over IoT

technologies stemming from their exposure to the

internet. The exposure can allow an attacker to tamper

with the data. Besides, the ever-increasing number of

IoT devices used in SG makes it more vulnerable to

attack (Kimani et al., 2019).

3 RELATED WORK

Security modelling has been carried out for the Smart

Grid but these studies either focused on a part of the

SG or they only partly covered the security controls.

Some topics in the cybersecurity design stage, such as

session mismanagement, have not been well

investigated. Many challenges relating to security are

still open. It is vitally important to develop an

appropriate model to address all the information

security challenges for the whole IoT-enabled SG.

Although the studies discussed the optimisation of

cost and performance, this research focuses on

identifying the main potential access points that are

vulnerable to internet-based threats in the SG, and all

relevant security controls that could mitigate the

internet-based threats and are applicable to each

access point, in a comprehensive modelling approach

that fills in the missing details in the NIST conceptual

model without considering their cost of

implementation.

4 PROPOSED MODEL

This section is focused on identifying the security

requirements, Threats, and controls that contribute to

the security of the SG information system.

4.1 Method for Model Development

This section charts the research roadmap to develop a

security model for IoT-enabled SG that fills in the

An Information Security Model for an IoT-enabled Smart Grid

159

lack of details on the NIST model. Figure 1 presents

the steps the research has undertaken for the

development process. In Step 1, the security

requirements were reviewed from international

industrial standards and from academic publications.

Then, both sets were combined and compared to

generate the Security Requirements. In Step 2, threat

modelling was carried out, based on the NIST

conceptual model to identify the access points. Then,

common internet-based threats were explored. Next,

security threats and requirements were both identified

using STRIDE analysis and classification. Step 3

assigned the identified threats to the access points,

according to functions and the information system

processed at each access point. In Step 4, the security

controls were grouped by the security requirements.

Finally, at Step 5 the security controls were mapped

to the access points by assessing threats effects to find

out the desired security requirements.

Figure 1: Development of the Security Model.

4.2 Security Requirements

The security requirements gleaned from literature and

industrial standards and authorities are reviewed and

analysed as the following (Mrabet et al., 2018;

Benmalek et al., 2019; Das and Zeadally, 2019;

Tufail et al., 2021):

1. Confidentiality: Ensuring that access to

transmitted data is restricted to authorised people. It

prevents the unauthorised disclosure of information.

In Smart Grid, the transmitted data could be sensitive,

such as personal information about a consumer’s

activities and billing data.

2. Integrity: Guarding the information and the

source of the information against any tampering or

unauthorised manipulation. Information could be

power measurements or price signals. A loss of

integrity may lead to false decision-making about

energy management.

3. Availability: Guarantee timely and reliable

access to the information (NISTIR 7628, 2014). The

power system needs to be available whenever

required by authorised entities. A loss of availability

may cause power cuts. Availability is about the

uptime and downtime of the SG system.

4. Authentication: Validating the identity of any

communicated entities (devices/users) in the SG. For

example, smart meters need to be authenticated so

that the utility company can bill the correct consumer.

Data authentication plays a significant role in proving

that the transmitted data are genuine, using

verification features such as digital signatures.

5. Authorisation: Granting the required rights to an

authenticated device/user to access SG resources. The

access control is that which guarantees that SG

resources are accessed by the correctly identified

entities.

6. Privacy: Guaranteeing that any private data

belonging to the consumer cannot be obtained

without permission and are used for pre-approved

purposes only. An attacker can extract information on

private data from the smart meter such as

consumption readings.

7. Non-repudiation: Assuring that the

accountability of any data transaction has been

undertaken between entities without any denial of

responsibility. It means assuring the traceability of

the system by recording each transaction by node,

device, consumer, and utility (Mrabet et al., 2018).

4.3 Internet-based Threats

Below are the common types of internet-based

cybersecurity threats found in the literature and

analysed using the STRIDE modelling technique as

described in the next section(Cisco, 2017; Marinos

and Lourenço, 2018; Mrabet et al., 2018; Otuoze et

al., 2018; Tonyali et al., 2018; Benmalek et al., 2019;

Das and Zeadally, 2019; Ganguly et al., 2019; Kimani

et al., 2019; Gunduz and Das, 2020; Tufail et al.,

2021):

1. Spoofing/Impersonation: This is an active attack

that aims to communicate on behalf of a legal entity

through unauthorised access, by stealing its identity.

An attacker may impersonate another’s smart meter

identity in order to pay lower electric charges – or let

the other pay.

Step 5. Map the Security Controls to the Access points

Step 5.1 Assess threat effect to

find out the desired Security

Requirements

Step 5.2 Assign the controls to

each Requirement

Step 4. Categorise the Controls by Security Requirement

Step 4.1 Review Security Controls

from literature, Microsoft

documentation, and standards [

Step 4.2 Use the

description of each

security control

Step 4.3 Group

the controls by

Requirement

Step 3. Assign Threats to the Access points

Step 3.1 Analyse each access point

according to their functionality,

operations processed, and

information systems located there

Step 3.2 Consider the

threats they could

encounter when processing

such operations

Step3.3 Review

the literature

Step 2. Threat Modelling

Step 2.1

Characterise the

system

Step 2.2 Identify

Assets and Access

points

Step 2.3 Exploring the

common internet-based

threats

Step 2.4 Apply

STRIDE analysis

and classification

Step 1. Generate the SG Security Requirements

Step 1.1 Review the

security requirements from

international industrial

standards

Step 1.2 Review the

security requirements

from published literature

Step 1.3 Compare and

combine the collected

security requirements

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

160

2. Eavesdropping/Traffic analysis/Man-In-The-

Middle (MITM): These are passive attack capturing

the transmitted data by intercepting the

communication between two entities in the SG. In

Traffic analysis, the attacker intercepts the

communication, analyses the network traffic, and

then extracts information from patterns to locate key

entities such as substations or disclose sensitive

information such as future price information.

3. Replay attack: A replay attack is an active attack

that intercepts the communication between two

entities by recording, observing, copying the

transmitted data, and then replaying a selected part of

the copied data back in an attack. It manipulates the

data before sending it back.

4. Data tampering: This strikes when an attacker

manipulates the exchanged data, such as dynamic

prices that are announced before peak times, making

them cheaper. Consequently, it can increase

consumer consumption instead of reducing it. This, in

turn, overloads the power network and causes power

outages.

5. Denial of Service (DOS)/Jamming channel:

This is an active attack that floods the entire system,

resources, or bandwidth, with a high number of fake

requests to overload the system, slow it, or corrupt

data transmission, thus making the SG unavailable.

This congested traffic prevents authorised entities

from accessing the system. A jamming channel attack

is a type of DOS threat. A distributed DOS (DDOS)

threat involves system servers or resources being

flooded by multiple attackers.

6. Malware injection: This is the execution of

malicious software on the SG, such as viruses,

spyware, rootkits, adware, malvertising, ransomware,

Trojan horses, or worms. It aims to damage, steal,

delete, modify, or disable, the main functions in smart

meters, or utility servers.

7. Phishing: Phishing that is included in this

research’s scope is internet-based Phishing such as

email Phishing and search engine/websites Phishing

that tricks users into believing that a message is from

a trustworthy organisation, asking them to click a

malicious link to obtain sensitive information. When

users respond, the attacker can use this information to

access the system (CISA, 2009).

8. SQL injections: A Structured Query Language

(SQL) injection executes a harmful SQL query

statement on the server that uses SQL, aiming to force

the server to disclose information, modify, or delete

the database contents. According to Cisco, this SQL

query is entered by the attacker using a website search

box on the client-side interface of the application and

is used to target database applications.

9. False data injection: This type of attack sends

fake information into the network, such as false meter

readings or wrong prices. It causes false state

estimation for the SCADA system and may cause a

power system failure. Thus, it influences the

electricity market financially by tampering with

market price information.

4.4 Threat Modelling

This research used the STRIDE technique for threat

modelling. Security requirements can be mapped to

threats to show the effect of each threat and the

required security criteria of the system. It is argued

that security requirements for the system can be

defined clearly once the threats are identified, as

shown in Figure 2. Threats are mapped to STRIDE

categories using STRIDE definitions and the threats

definitions of this research provided at section 4.3.

Each identified threat is mapped to STRIDE

categories based on the main effect of that threat at

the first instance. Then, threats are mapped to security

requirements based on STRIDE mapping as well as

the literature (Mrabet et al., 2018; Stellios et al.,

2018; Gunduz and Das, 2020; Tufail et al., 2021).

Security controls are countermeasures to mitigate,

delay or prevent threats in order to strengthen the

information system against threats. The controls are

approaches that ensure security requirements. The

security controls are taken from the literature and

Microsoft documentation (2009). Security controls

are then categorised by security requirements using

the description of each security control, as shown

Table 1 at appendix A. In addition, all the standards,

including NIST IR, NERC CIPS (1-9), NIST IR7628,

and NIST SP 800-53, are reviewed as well as the

publications (Mrabet et al., 2018; Das and Zeadally,

2019; Ganguly et al., 2019; Kimani et al., 2019) to

map the security controls to the security

requirements.

4.5 Identifying Access Points

This step articulates the main access points that are

vulnerable to internet-based threats in the IoT-

enabled SG by reviewing publications, and the

vulnerability analysis compiled by the U.S. electric

sector issued by Idaho National Laboratory (Glenn

An Information Security Model for an IoT-enabled Smart Grid

161

Security

Requirements

Threats

STRIDE

Confidentiality

Integrity

Availability

Authentication

Authorization

Privacy

Non-Repudiation

Spoofing

Tampering

Repudation

Information

disclosure

Denial of service

Elevation of

privilege

Spoofing/Impersonation

Eavesdropping/Traffic

analysis/ (MITM)

Replay attack

Data tampering

Denial of Service (DOS)/

Jamming channel

Malware injection

Phishing

SQL injections

False data injectio

Figure 2: SG Threat modelling.

Figure 3: The proposed Security Model.

et al., 2017). Figure 3 Shows seven access points that

are most likely to be exploited to execute cyber-

attacks: (1) Smart Meters and Smart Appliances; (2)

Transmission Stations, Distribution Substations, and

Smart automation devices for transmission and

distribution (Switches, Sensors, Actuators,

Transformers, Voltage regulator, Capacitors); (3)

Generation Plant and Information Communication

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

162

Technology (ICT) Systems; (4) Advanced Metering

Infrastructure (AMI); (5) SCADA (Supervisory

Control and Data Acquisition)/SAS (Substations

Automation Systems)/ Control Centre; (6) Utility

data centre; (7) Market.

5 CONCLUSIONS

In conclusion, the proposed security model shown in

Figure 3 consists of seven security requirements, nine

threats, seven access points, and thirty-eight security

controls.

The model addresses the limitation found in the

NIST model as NIST is a very high-level conceptual

model lacking details that make the proposed model

more practical and useful for the related sectors to

use.

This research will be beneficial to system

designers, information security practitioners, and

stakeholders to consider the key requirements and

challenges, identify the security threats and

vulnerabilities, and maintain the required

mechanisms through the initial stages of the

development of a SG system design.

For the future work, the next phase of this research is

to have the model validated by experts in the industry

including threats, access points, requirements, and

controls. The initial reviews confirmed this model

and the importance of it to support the energy sector

towards securing automated Smart Grids. Then, the

model will be verified by formal modelling.

REFERENCES

Al-Ali, A.R. and Aburukba, R. (2015) ‘Advanced role of

internet of things in the smart grid technology’, Journal

of Computer and Communications, pp. 229–233.

Al-Turjman, F. and Abujubbeh, M. (2019) ‘IoT-enabled

smart grid via SM: An overview’, Future Generation

Computer Systems, 96, pp. 579–590.

Aloul, F.A. (2012) ‘The Need for Effective Information

Security Awareness’, Journal of Advances in

Information Technology, 3(3), pp. 176–183.

Bedi, G., Venayagamoorthy, G.K., Singh, R., Brooks, R.R.

and Wang, K.C. (2018) ‘Review of Internet of Things

(IoT) in Electric Power and Energy Systems’, IEEE

Internet of Things Journal, 5(2), pp. 847–870.

Bekara, C. (2014) ‘Security issues and challenges for the

IoT-based smart grid’, Procedia Computer Science, 34,

pp. 532–537.

Benmalek, M., Challal, Y. and Derhab, A. (2019)

‘Authentication for Smart Grid AMI Systems: Threat

Models, Solutions, and Challenges’, Proceedings - 2019

IEEE 28th International Conference on Enabling

Technologies: Infrastructure for Collaborative

Enterprises, WETICE 2019, pp. 208–213.

CISA (2009) ‘Understanding Digital Signatures | CISA’.

Cisco (2017) What Is a Cyberattack? - Most Common

Types - Cisco. Available at: https://www.cisco.com/

c/en/us/products/security/common-cyberattacks.html

(Accessed: 21 February 2020).

Dagle, J.E. (2012) ‘Cyber-physical system security of smart

grids’, 2012 IEEE PES Innovative Smart Grid

Technologies, ISGT 2012, pp. 1–2.

Dalipi, F. and Yayilgan, S.Y. (2016) ‘Security and privacy

considerations for IoT application on smart grids:

Survey and research challenges’, Proceedings - 2016

4th International Conference on Future Internet of

Things and Cloud Workshops, W-FiCloud 2016, pp.

63–68.

Das, A.K. and Zeadally, S. (2019) Data Security in the

Smart Grid Environment, Pathways to a Smarter Power

System. Elsevier Ltd. doi:10.1016/B978-0-08-102592-

5.00013-2.

EPRI Electric Power Research Institute (2005) EPRI Smart

Grid Resource Center. Available at:

https://smartgrid.epri.com/.

Ganguly, P., Nasipuri, M. and Dutta, S. (2019) ‘Challenges

of the Existing Security Measures Deployed in the

Smart Grid Framework’, Proceedings of 2019 the 7th

International Conference on Smart Energy Grid

Engineering, SEGE 2019, pp. 1–5.

Ghasempour, A. (2019) ‘Internet of things in smart grid:

Architecture, applications, services, key technologies,

and challenges’, Inventions, 4(1)..

Glenn, C., Sterbentz, D. and Wright, A. (2017) Cyber

Threat and Vulnerability Analysis of the U.S. Electric

Sector, Inl (Idaho National Laboratory).

Gunduz, M.Z. and Das, R. (2020) ‘Cyber-security on smart

grid: Threats and potential solutions’, Computer

Networks, 169, p. 107094.

He, D., Kumar, N., Chen, J., Lee, C.C., Chilamkurti, N. and

Yeo, S.S. (2013) ‘Robust anonymous authentication

protocol for health-care applications using wireless

medical sensor networks’, Multimedia Systems, 21(1),

pp. 49–60.

IEEE (2018) About - IEEE Smart Grid. Available at:

https://smartgrid.ieee.org/about-ieee-smart-grid

(Accessed: 4 December 2019).

Al Khuffash, K. (2018) Smart grids—Overview and

background information, Application of Smart Grid

Technologies. Elsevier Inc. doi:10.1016/b978-0-12-

803128-5.00001-5.

Kimani, K., Oduol, V. and Langat, K. (2019) ‘Cyber

security challenges for IoT-based smart grid networks’,

International Journal of Critical Infrastructure

Protection, 25, pp. 36–49..

Mahmood, K., Ashraf Chaudhry, S., Naqvi, H., Shon, T.

and Farooq Ahmad, H. (2016) ‘A lightweight message

authentication scheme for Smart Grid communications

in power sector’, Computers and Electrical

Engineering, 52, pp. 114–124.

An Information Security Model for an IoT-enabled Smart Grid

163

Manyika, J., Chui, M., Bisson, P., Woetzel, J., Dobbs, R.,

Bughin, J. and Aharon, D. (2015) Unlocking the

potential of the Internet of Things | McKinsey &

Company, McKinsey.

Marinos, L. and Lourenço, M. (2018) ENISA Threat

Landscape Report 2018 15 Top Cyberthreats and

Trends, European Union Agency For Network and

Information Security. doi:10.2824/622757.

Mrabet, Z. El, Kaabouch, N., Ghazi, Hassan El and Ghazi,

Hamid El (2018) ‘Cyber-security in smart grid: Survey

and challenges’, Computers and Electrical Engineering,

67, pp. 469–482.

NIST (2014) NIST Special Publication 1108R3 NIST

Framework and Roadmap for Smart Grid

Interoperability Standards, Release 3.0, NIST Special

Publication. doi:10.6028/NIST.SP.1108r3.

NISTIR 7628 (2014) NISTIR 7628 Guidelines for Smart

Grid Cyber Security, Revision 1, NIST.

Otuoze, A.O., Mustafa, M.W. and Larik, R.M. (2018)

‘Smart grids security challenges: Classification by

sources of threats’, Journal of Electrical Systems and

Information Technology, 5(3), pp. 468–483.

Reka, S.S. and Dragicevic, T. (2018) ‘Future effectual role

of energy delivery : A comprehensive review of

Internet of Things and smart grid’, Renewable and

Sustainable Energy Reviews, 91 (April), pp. 90–108.

doi:10.1016/j.rser.2018.03.089.

Saleem, Y., Crespi, N., Rehmani, M.H. and Copeland, R.

(2019) ‘Internet of Things-Aided Smart Grid:

Technologies, Architectures, Applications, Prototypes,

and Future Research Directions’, IEEE Access, 7, pp.

62962–63003.

Stellios, I., Kotzanikolaou, P., Psarakis, M., Alcaraz, C. and

Lopez, J. (2018) ‘A survey of iot-enabled cyberattacks:

Assessing attack paths to critical infrastructures and

services’, IEEE Communications Surveys and

Tutorials, 20(4), pp. 3453–3495.

Tonyali, S., Akkaya, K., Saputro, N., Uluagac, A.S. and

Nojoumian, M. (2018) ‘Privacy-preserving protocols

for secure and reliable data aggregation in IoT-enabled

Smart Metering systems’, Future Generation Computer

Systems, 78, pp. 547–557.

Tufail, S., Parvez, I., Batool, S. and Sarwat, A. (2021) ‘A

survey on cybersecurity challenges, detection, and

mitigation techniques for the smart grid’, Energies,

14(18), pp. 1–22.

U.S. Department of energy (2018) Smart Grid System

Report: 2018 Report to Congress.

APPENDIX A

Table 1: Mapping security controls to security requirements.

Security

uirement

Security controls Code

Authentication

(Aun)

1. Keyed cryptographic hash functions (HMAC), digital signatures, and Random numbers

generators

Aun1

2. Physically Unclonable Functions (PUF) Aun2

3. MAC-attached, and HORS-signed messages Aun3

4. Secure Sockets layer Certificates (SSL Certificates) and Transport Layer Security

(TLS)

Aun4

5. Multi-factor authentication mechanism Aun5

6. Automatic lockouts Aun6

Authorisation

(Aur)

7. Attribute-Based Encryption Aur1

8. Attribute Certificates Aur2

9. Attribute-Based Access Control System based on XACML (Extensible Access Control

Markup Language)

Aur3

10. Role-Based Access Control and allow/block listing Aur4

Confidentiality (C) 11. Symmetric and asymmetric algorithms and Public Key Infrastructure certificate (PKI) C1

Privacy

(P)

12. Anonymisation P1

13. Trusted aggregators P2

14. Homomorphic encryption P3

15. Perturbation models P4

16. Verifiable computation models, and zero-knowledge proof systems P5

17. Data obfuscation techniques P6

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

164

Table 1: Mapping security controls to security requirements (cont.).

Security

requirement

Security controls Code

Integrity

(In)

18. Cryptographic hashing functions and session keys

In1

19. Digital watermarking

In2

20. Automated patch management for flaw remediation

In3

21. Adaptive cumulative sum algorithm

In4

22. Secure Phasor Measurement Units (PMUs) installation

In5

23. Load profiling algorithms

In6

24. Timestamps

In7

25. Sequence numbers

In8

26. Query sanitisation

In9

27. Nonces

In10

Availability

(Av)

28. Use multiple alternate frequency channels according to a hardcoded sequence

Av1

29. Frequency quorum rendezvous between connected nodes

Av2

30. Anomaly Intrusion Detection Systems (IDS)

Av3

31. Specification-based IDS

Av4

32. Intrusion Prevention Systems (IPS)

Av5

33. Quality of Services (QoS)

Av6

34. Load balancing

Av7

35. Operating system-independent Applications

Av8

Non-repudiation

(N)

36. Mutual Inspection technique

37. Unique keys and digital signatures

38. Transaction log

An Information Security Model for an IoT-enabled Smart Grid

165