Identifying Threats Cybercrime and Digital Forensic Opportunities in Smart City Infrastructure via Threat Modeling

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Identifying Threats, Cybercrime and Digital

Forensic Opportunities in Smart City Infrastructure

via Threat Modeling

Yee Ching Tok ID

Singapore Univ. of Tech. and Design

Singapore

yeeching_tok@sutd.edu.sg

Sudipta Chattopadhyay ID

Singapore Univ. of Tech. and Design

Singapore

sudipta_chattopadhyay@sutd.edu.sg

Abstract—Technological advances have enabled multiple coun-

tries to consider implementing Smart City Infrastructure to

provide in-depth insights into different data points and enhance

the lives of citizens. Unfortunately, these new technological

implementations also entice adversaries and cybercriminals to

execute cyber-attacks and commit criminal acts on these modern

infrastructures. Given the borderless nature of cyber attacks,

varying levels of understanding of smart city infrastructure and

ongoing investigation workloads, law enforcement agencies and

investigators would be hard-pressed to respond to these kinds of

cybercrime. Without an investigative capability by investigators,

these smart infrastructures could become new targets favored by

cybercriminals.

To address the challenges faced by investigators, we propose

a common deﬁnition of smart city infrastructure. Based on the

deﬁnition, we utilize the STRIDE threat modeling methodology

and the Microsoft Threat Modeling Tool to identify threats

present in the infrastructure and create a threat model which

can be further customized or extended by interested parties.

Next, we map offences, possible evidence sources and types of

threats identiﬁed to help investigators understand what crimes

could have been committed and what evidence would be required

in their investigation work. Finally, noting that Smart City

Infrastructure investigations would be a global multi-faceted

challenge, we discuss technical and legal opportunities in digital

forensics on Smart City Infrastructure.

I. INTRODUCTION

In this increasingly interconnected world, humans generate

a lot of data in their daily lives through the usage of com-

puting devices and the multitude of increasingly accessible

technologies that enable a better quality of life. The technolo-

gies include using smart home devices (popularly termed as

Internet-of-Things (IoT)) and interaction with novel technical

implementations by governments to improve citizens’ quality

of life. These novel technical implementations range from

smart water and electricity meters, smart vehicles, autonomous

vehicles and building automation systems [1]. These govern-

ments’ ﬁnal goal was to presumably expand the systems into

Smart City Infrastructure (SCI) for a better overview of their

citizens, environment, safety and resources. Figure 1 illustrates

how governments and city planners could gather various data

points to obtain a holistic overview of the country, allowing

them to provide timely governance intervention where needed.

Citizen Services

Health

Livelihood Support Essential Services Resources

Water Power

Safety

Data DataData

Environmental

and Climate Change

Urban

Planning

EducationEconomy

Data Consolidation

Data

Data Collection (via Smart City technology/sensors/manual input)

Smart City

Dashboard

Analytics

Computational

Resource

Central

Database

Intermediary

Data

Real-time

City

Statistics

Government Officials/

City Planners

Raw City

Data

Fig. 1. Overview of Smart City Infrastructure (SCI) Data Collection and

Processing

Unfortunately, governments and city planners are not the

only ones embracing the adoption of technologies related

to SCI. These new systems offer attractive opportunities to

state-sponsored adversaries and cybercriminals. If SCI data

could be stolen or intercepted, adversaries and cybercriminals

could gain access to a plethora of data that could be further

exploited. For example, the data could be exploited to cause

economic issues to a target country (e.g., sudden restrictions

on export of resources or medical supplies) or events intended

to destabilize the country (e.g., sudden outbreak of diseases

while a country’s hospitals are nearing maximum capacity).

The adversaries could cause further chaos if they successfully

breach SCI systems and trigger anomalous conditions (e.g.,

opening/closing valves in critical infrastructure or overriding

safety mechanisms) to cause emergency shutdowns, destruc-

tion of facilities or general chaos in transport systems.

Digital forensic investigators (DFI) and law enforcement

agencies (LEA) have been crucial in investigating cyberattacks

and cybercrime. However, DFI and LEA will likely require

further support if they are called upon to investigate attacks

on SCI as described earlier. This is because DFI and LEA per-

sonnel are typically not familiar of the unique characteristics

of SCI systems as compared to conventional digital systems.

arXiv:2210.14692v2 [cs.CR] 16 Mar 2023

Moreover, DFI and LEA are likely to have other ongoing

digital investigations. Their ongoing case commitments may

prevent them from being able to perform research on SCI

systems to identify the required evidence. Meanwhile, SCI

system owners may also be unable to provide the necessary

evidence since these requirements were not speciﬁed in the

ﬁrst place when SCI was implemented. Caviglione et al.

[2] stressed that digital forensic investigation on IoT devices

would have to be performed on small-scale implementations

(such as homes) to large-scale deployments such as those

within a smart city. It was further stated that the technology

used to build the infrastructure would be diverse and that

their accessibility features must be discerned and appraised

independently for forensic investigations [2]. The predicament

has not gone unnoticed - there have been calls for further

digital forensic research on new digital artifacts and preventing

misinterpretations of artifacts [3].

Digital forensics and cybersecurity in smart cities had been

previously discussed, where the deﬁnition of smart cities

was based on National Institute of Standards and Technology

(NIST) and only focused on smart environments, living and

mobility [1]. While the threats, forensic data, and data sources

highlighted did provide some guidance for DFI and LEA [1],

the provided information cannot apply to all SCI internation-

ally. For example, different countries have different technical

requirements, implementation and data needs for their SCI.

This could prove problematic for solutions that suggest a one-

size-ﬁts-all approach by speciﬁcally listing functional systems,

such as the ones suggested by Baig et al. [1].

Due to the complexity of SCI, digital evidence for various

components of SCI must be identiﬁed before actual cybercrime

occurs. This is to reduce the stress faced by DFI and LEA as

ﬁrst responders to cybercrime. DFI and LEA could also better

handle SCI cybercrime challenges if a standard deﬁnition of

SCI is achieved, along with potential threats, offences and

evidence sources pre-identiﬁed. Although this is a global

challenge, adhering to global initiatives and standards allow

ﬂexibility of adoption by international DFI and LEA.

The contributions of our research are summarized as fol-

lows:

1) We highlight current issues in SCI and deﬁne a standard-

ized deﬁnition of SCI.

2) We develop and make publicly available our threat model

template to governments, DFI and LEA to identify threats

in SCI.

3) We map SCI threats to possible offences and correspond-

ing SCI evidence sources and types.

4) We discuss future SCI digital forensics opportunities from

a technical and legal perspective.

For reproducibility and advancing the research in SCI

digital forensics and threat modeling, our threat model

is publicly available at: https://github.com/poppopretn/

SmartCityThreatModel.

The rest of this paper is organized as follows. In Section II,

we present the context of the paper and deﬁne SCI. In

Section III, we highlight the choice of our threat modeling

methodology, showcase our threat model and present the

threats identiﬁed in SCI. In Section IV, we show the threats,

offences, evidence sources and types within SCI that we

derived using our threat model. In Section V, we discuss future

technical and legal opportunities for SCI digital forensics. In

Section VI, we explain the limitations of our research. In

Section VII, we summarize current related work in SCI digital

forensics. Finally, we conclude the paper in Section VIII.

II. CONTEXTUALIZING SMART CITY INFRASTRUCTURE

Technological innovations and rapid deployment of Internet

of Things (IoT) devices have transformed many cities in

different geographical regions into smart cities [4]. Arguably,

these cities may not have implemented sufﬁcient infrastructure

that can deliver futuristic societal outcomes such as accident-

free environments or zero-waste scenarios. However, these

current implementations have brought about positive changes

such as moulding the design of future cities and achieving

sustainable use of resources [4].

A. Current Issues in Smart City Infrastructure

The implementation of SCI is an attractive option for gov-

ernments looking to improve citizens’ lives and has increased

visibility to critical indicators such as resource utilization and

public safety. Nonetheless, there are multiple challenges to

implement such capabilities as outlined below:

1) Deﬁnition Issues: It is vital to set the right context and

deﬁnition when SCI is discussed. From the academic

perspective, a commonly agreed deﬁnition of a Smart

City has yet to be agreed on. A brief literature review

of papers regarding SCI was conducted and yielded at

least three different deﬁnitions of a Smart City [5]–[7].

SCI is not clearly deﬁned from the industry perspective

either. Various industry solutions such as Bosch [8],

Cisco Kinetic for Cities [9], Microsoft CityNext [10]

and Schneider Electric EcoStruxure [11] have offered

products touted to allow prospective customers to create

smart cities. However, a review of their respective product

briefs showed that these solutions appear not to be based

on any commonly agreed upon deﬁnitions or standards.

Many also fail to realize that such forms of deﬁnition are

constrained by ﬁnancial budgets and technological matu-

rity of the location SCI are deployed in. This inevitably

contributes to the problem of a lack of standard deﬁnition

in SCI.

2) Interoperability Issues: This is an extension of the

deﬁnition issue mentioned previously. There were at least

31 different vendors [12] offering various platforms and

technologies to build SCI. It is unlikely that any one

vendor could meet all the design requirements (hard-

ware and software) of a city/country. A more realistic

outcome would be a myriad of vendors being chosen

to implement technologies by their respective strengths.

With such a gamut of sensors, protocols, and tech-

nologies, interoperability between vendors becomes an

issue. Although entities such as FIWARE [13] and the

TALQ Consortium [14] offer Application Programming

Interfaces (APIs) to allow interoperability of technologies

with different vendors, adoption and implementation of

such APIs remain unclear.

3) Cybercrime Issues: Cyber attacks on smart cities could

become the next issue governments face as such projects

are implemented. From the legal perspective, respective

laws possibly were not updated to include attacks on

SCI. Meanwhile, from the law enforcement and incident

response perspective, there could be a lack of experience,

knowledge and training for professionals called upon to

investigate such attacks. This issue is further exacerbated

by the deﬁnition and interoperability issues. There is

no standard deﬁnition of a smart city, and multiple

technologies are being utilized in a smart city. Legal,

remediation and law enforcement actions are hampered

due to varying understanding of SCI and technology

complexities, leading to a risk of misleading evidence

being retrieved and presented to courts of law.

A properly deﬁned and widely accepted deﬁnition of SCI

could address the issues highlighted above. For example,

a properly deﬁned SCI will facilitate and empower Digital

Forensic Investigators (DFI) in peer review processes, espe-

cially at Levels 3 and 4 of the Peer Review Hierarchy as

proposed by [15]. It also facilitates interoperability between

vendors and enhances the development of APIs. Finally, it

enhances clarity in connections between evidence and criminal

hypotheses, reducing the risks of misleading evidence being

presented in courts [16].

B. Deﬁning Smart City Infrastructure

The primary issue originates from a lack of a standard

deﬁnition in SCI as various vendors and entities are vying to

be the standard for smart cities. Geographical differences and

individual governmental requirements have not helped foster

a standard deﬁnition of a smart city. A way to transcend such

challenges in deﬁning a smart city, along with relevant data

indicators is required to facilitate the resolution of issues raised

in Section II-A.

As a body that strives to standardize methods to accomplish

a ﬁnal goal, the International Organization for Standardization

(ISO) facilitates such an endeavour. After extensive research,

we identiﬁed multiple ISO standards that provided a suitable

framework for a standardized deﬁnition of SCI. With reference

to Figure 2, the ISO standards that were selected are as

follows:

1) ISO37101:2016 [17]

2) ISO37120:2018 [18]

3) ISO37122:2019 [19]

4) ISO37123:2019 [20]

As observed from Figure 2, our proposed SCI deﬁnition (i.e.

the body) is guided by four ISO standards. ISO37101:2016

serves as the guiding vision of a Smart City (i.e., a skele-

ton) via sustainable development. Concurrently, correspond-

ing core and supporting data indicators from Factor #1

Vision:

Sustainable development in communities (ISO37101:2016)

Factor #1:

Indicators for City

Services and

Quality of Life

(ISO37120:2018)

Factor #2:

Indicators for

Smart Cities

(ISO37122:2019)

Factor #3:

Indicators for

Resilient Cities

(ISO37123:2019)

Smart

City Infrastructure (SCI) Definition

Fig. 2. Deﬁning SCI

(ISO37120:2018), Factor #2 (ISO37122:2019) and Factor #3

(ISO37123:2019) provide the required context of the SCI (i.e.,

the muscles/ﬂesh). Core data indicators are mandatory data

indicators that must be captured if the ISO standards are used,

whereas supporting data indicators are recommended to be

captured (but not mandatory). We further explain our choice

as follows:

1) ISO37101:2016 - In contrast to a technical view and

deﬁnition of SCI, the standard ISO37101:2016 adopts

a technology-agnostic approach and uses sustainable de-

velopment as a common denominator before any form

of technical implementation is utilized. Table I lists

the underlying fundamental purposes of sustainability in

modern society along with sustainability issues raised in

ISO37101:2016. Since the issues are systematically listed

out before any form of smart city technology is imple-

mented, ISO37101:2016 serves as a suitable component

to drive the vision aspect of our proposed SCI deﬁnition

(see Figure 2).

TABLE I

SUSTAINABILITY PURPOSES AND ISSUES HIGHLIGHTED IN

ISO37101:2016

Purposes of Sustainability Sustainability Issues

1. Attractiveness (e.g., Culture,

identity)

2. Preservation and improvement

of environment (e.g., Protection

of biological diversity and ecosys-

tem)

3. Resilience (e.g., Climate change

adaptation, economic shock pre-

paredness)

4. Responsible resource use (e.g.,

Sustainable production, reusing

and recycling of materials)

5. Social cohesion (e.g., Diversity,

sense of belonging, social mobil-

ity)

6. Well-being (e.g., Happiness,

healthy environment, quality of

life)

1. Governance, empowerment and

engagement

2. Education and capacity building

3. Innovation, creativity and re-

4. Health and care in the commu-

nity

5. Culture and community identity

6. Living together, interdependence

and mutuality

7. Economy and sustainable pro-

duction and consumption

8. Living and working environment

9. Safety and security

10. Community infrastructures

11. Mobility

12. Biodiversity and ecosystem ser-

vices

2) ISO37120:2018, ISO 37122:2019 and ISO37123:2019

- Building on the technology-agnostic approach of

ISO37101:2016, core and supporting data indicators

with respect to measuring city services and quality

of life (ISO37120:2018), smart cities (ISO37122:2019)

and resiliency (ISO37123:2019) are outlined. The data

indicators used in the three ISO standards are listed

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IdentifyingThreats,CybercrimeandDigitalForensicOpportunitiesinSmartCityInfrastructureviaThreatModelingYeeChingTokIDSingaporeUniv.ofTech.andDesignSingaporeyeeching_tok@sutd.edu.sgSudiptaChattopadhyayIDSingaporeUniv.ofTech.andDesignSingaporesudipta_chattopadhyay@sutd.edu.sgAbstractTechnologicaladvanc...

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Identifying Threats Cybercrime and Digital Forensic Opportunities in Smart City Infrastructure via Threat Modeling

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