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Dynamic assessment of regulation and policy framework in the cybersecurity of
Connected and Autonomous Vehicles
Shah Khalid Khan1, Nirajan Shiwakoti1, Peter Stasinopoulos1, Matthew Warren2
1School of Engineering, RMIT University Melbourne, Australia
2RMIT Centre for Cyber Security Research and Innovation, RMIT University, Melbourne,
Australia
Email for correspondence: s3680269@student.rmit.edu.au; shahkhalid_k@yahoo.com
Abstract
CAVs (Connected and Autonomous Vehicles) technology will transform the current Intelligent
Transportation System (ITS). However, the most significant challenge is keeping up the
criminal justice system, particularly the Regulation and Policy Framework (R&PF) in ITS,
because ubiquitous CAVs connectivity expands the scope of criminal activity in both the
physical and cyberspace realms. This article developed a Causal Loop Diagram-based System
Dynamic model that incorporates critical inter-disciplinary parameters and dynamically
evaluates the impact of R&PF on CAVs cybersecurity. Two loops are envisioned: "balancing
loops" demonstrate how R&PF can facilitate cyber-attacks prevention, whereas "reinforcing
loops" reveal how imposing R&FP can negate its potential benefits by creating a detrimental
parallel circle. Based on the feedback loops, a "shifting the burden" system archetype is
postulated in which governments combat cyber-threats by strengthening R&PF while also
reducing CAVs adaptation through imitation and induction. Recommendations for R&PF
formulation include a balanced approach to the trade-off between: i) protection of CAV users'
privacy and freedom, ii) operational and data accessibility constraints on CAV automakers and
service providers, as well as their business investment protection, and iii) state regulators
command and control thresholds.
1. Introduction
The introduction of CAVs (Connected and Autonomous Vehicles) will fundamentally alter
the existing transportation landscape. CAVs at Levels 4 and 5 can detect their surroundings
and navigate autonomously by using: i) a variety of sensors (camera, radar, lidar), ii) 3D
holographic display, and iii) vast amounts of data analysed in the Intelligent Transportation
System (ITS) (SAE-International, 2018). The evolution of this transition necessitates that the
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CAV be administered as a cyber-physical system-a collaborative network of electronic
components that regulate mechanical elements. This has prompted new partnerships between
technology companies and traditional automakers, broadening the scope of criminal activity in
both the physical and cyberspace (Khan et al., 2020e).
The CAVs deployment is expected to result in various economic benefits, including more
equitable transportation, improved public health and social welfare (Haratsis et al., 2018),
reduced environmental degradation, and increased road safety . Despite these advantages, a
significant source of concern for CAVs stakeholders (technology companies, financial
institutions, users and automakers) is a lack of clarity in Regulation and Policy Frameworks
(R&PF)-which could impede the widespread CAVs roll-out (Khan et al., 2021a). The present
legal framework is seen as a barrier to the potential community advantages of CAVs in terms
of safety, productivity, environmental, and mobility improvements (Dosen et al., 2017, Khan
et al., 2021a). The primary source of this anxiety is the ramification caused by the ubiquitous
CAV connectivity within the ITS, which has resulted in a slew of regulatory caveats across
multiple jurisdictions. These avenues are: i) data confidentiality, privacy concerns, and
operational safety during CAVs operation in ITS, ii) short-term economic considerations,
public infrastructure, as well as social and economic issues surrounding private car ownership,
ridesharing, and employment, and iii) insurance liability in the event of an accident.
Moreover, in the event of a successful cyber-attack, these concerns would be heightened, i.e.,
determining the liability and accountability of perpetrators in the dynamically integrated CAV-
based ITS operation is highly challenging. Cyber-attackers have been dubbed "modern-day
pirates," and cyber-insurance has been proposed. It is, however, a two-edged sword: it is both
a part of the problem and a solution. It would ease the cost of corporate investment while also
providing a veneer of legitimacy for ransom payments.
R&PF are critical components of CAVs cybersecurity. While CAVs Levels 3-5 are still being
tested, regulatory authorities should develop standards for CAV deployment and take the
necessary steps to ensure the cyber-safe operation of CAVs in ITS (Noy et al., 2018). However,
the regulation is challenging when the commercialization of the end-user's data results in profit
for some parties at the expense of the end-user. Additionally, the integration of technology and
automakers necessitates a dynamic and interconnected analysis of CAVs R&PF. Nonetheless,
the criminal justice system is struggling to keep up with technological advancements in
transportation.
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1.1 The rationale of the study
Academics and industry are equally interested in the R&PF that governs the functioning of
CAVs in ITS. Numerous researchers have assessed and emphasised the importance of
regulations governing CAVs cybersecurity in various aspects (Cabinet Office, 2016, Hodge et
al., 2019). Nevertheless, current research lacks a system-wide foresight analysis that employs
the System Dynamics (SD) approach to dynamically assess the R&PF governing CAVs
cybersecurity in ITS (Sterman, 2000, Khan et al., 2021a, Khan et al., 2022c).
There is "nested complexity" when a sophisticated organisational (ITS) and policymaking
structure (regulators) governs a physical system (CAV). Understanding "nested complexity"
is an important first step towards properly integrating the three components (technology,
organisations, and policy) for a cyber-safe CAV-based ITS. It will highlight the critical role of
R&PF in CAVs cybersecurity and its implications on CAVs adoption. To the best of
knowledge, the scope of R&PF in CAVs cybersecurity has not yet been synthesised in a
fundamental system-oriented approach. CAVs in the ITS are highly complex and dynamic,
involving many stakeholders and a large number of interactions between them.
1.2 Causal Loop Diagram
The Causal Loop Diagram (CLD) based SD approach-a subset of system theory seeks to
synthesise and comprehend complex systems' behaviour. CLDs are analysed in terms of
behaviour patterns, with more intuition and more profound knowledge (Forrester, 1958,
Sterman, 2000). Because CAV technology is constantly evolving, and there is a scarcity of
empirical data on the use of R&PF in CAV cybersecurity. Therefore, CLD is a suitable research
technique for developing a unified suite of high-leverage technologies and policies for CAVs
cybersecurity.
The authors (Khan et al., 2021a) developed a conceptual SD model to analyse cybersecurity
in the complex, uncertain deployment of CAVs. Specifically, the SD model integrates six
critical avenues and maps their respective parameters that either trigger or mitigate cyber-
attacks in the operation of CAVs using a systematic theoretical approach. These six avenues
are: i) CAVs communication framework, ii) secured physical access, iii) human factors, iv)
CAVs penetration, v) regulatory laws and policy framework, and iv) trust—across the CAVs-
industry and among the public. Similar the same authors (Khan et al., 2022a) proposes a CLD-
based SD model that incorporates key inter-disciplinary variables and evaluates the impact of
TM on AVs' cybersecurity in a dynamic and integrated manner. Moreover, Khan et al. (2022c)
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developed an SD model for strategic cybersecurity assessment in the CAVs roll-out. The SD
model incorporates a Stock-and-Flow Model, which can integrate multiple perspectives into a
single model and map various parameters that either stimulate or prevent cyber-attacks by
integrating the critical elements of CAV cybersecurity.
1.3 Contribution of the study
We conducted an integrated dynamic evaluation of the impact of R&PF on the cybersecurity
of CAVs. The key contributions are listed below:
➢ We developed a CLD-based SD model that incorporates key inter-disciplinary factors
pertinent to the R&PF in CAVs cybersecurity.
➢ Based on the CLD, we identified two types of feedback loops: i) "balancing loops,"
which show how R&PF may aid in cyber-attack prevention, and ii) "reinforcing loops,"
which demonstrate how enforcing R&FP can negate its potential advantages by
generating a destructive parallel cycle.
➢ Balancing loops and reinforcing loops triggered a system archetype-"shifting the
burden". The system archetype illuminates the underlying structures, providing natural
leverage for successful system modifications.
➢ Additionally, we proposed recommendations for developing an appropriate R&PF for
CAVs cybersecurity.
The remainder of the paper is structured as follows. The next section outlines the methodology
adopted. Section 3 elaborates the conceptual CLD-based SD Model. The following section
describes the feedback loops and system archetype. Section 5 focuses on discussion and policy
recommendations. The limitations and future extensions of the study are finally presented in
Section 6.
2. Methodology
To investigate the complex, interconnected, and uncertain impact of R&PF on CAVs
cybersecurity, we used a CLD-based SD approach, a technique that has the potential to
investigate the system-level cybersecurity implications of self-driving cars (Sterman, 2000,
Stasinopoulos et al., 2020, Khan et al., 2022c, Khan et al., 2021a). CLD visualises model
composition using intuitive graphical diagrams, determines key factors, generates feedback
loops, and identifies a system archetype. The use of system archetypes enables the efficient
improvement of systems. System archetypes may be used as a diagnostic tool to identify
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behavioural patterns that have developed an undesirable situation. CLD is used to assess the
security of SAE 4 (or higher) self-driving vehicles through the lens of a functional pathway
(SAE-International, 2018).
3. Proposed structure of the Causal Loop Diagram
The model variables and their mapping are based on solid innovation theory, i.e., meta-
exploratory quantitative analysis of post-2010 literature derived from various sources,
including peer-reviewed journal databases, books, and doctoral dissertations, and credible
company surveys; augmented with forward/backward snowballing. This leads to identifying
critical avenues that are crucial to assess the impact of R&PF on CAVs cyber-safety research.
The following section discusses each parameter's scope, significance, and influence in the
model and provides references.
Additional aspects such as CAVs communication technology, human considerations, trust,
and hacker capability are outside the scope of this study. This choice is mainly motivated by
the need for a restricted border when assessing limited parameters, as well as the non-
geographical character of the SD model, although these features are essential for future
research.
3.1 The Causal Loop Diagram (CLD) development
The CLD consists of nodes and edges. Nodes represent the variables, and edges represent
the relationships between the variables. In a positive causal relationship, both nodes increase
in the same direction. In contrast, in a negative relationship, as one node grows, the other
decreases, thus implying that the two variables move in opposite directions. The two closed
cycles, reinforcing and balancing, are essential features of CLDs. Reinforcing loop: change in
one direction is compounded by additional change, and balancing loop: change in one direction
is countered by a change in the opposite direction. The arrow with two small lines indicates the
presence of a delay sign.
The proposed architecture of the CLD for R&PF in the CAVs cybersecurity is depicted in
Figure 1. The scope of various variables included in CLD (Figure 1) is summarised in a
consolidated tabular description, along with references in Table 1. The linking of independent
and dependent variables in terms of cause and effect, potential impact as a process, polarity
(positive or negative influence), and uncertainty is shown in Table 1. Because there is a dearth
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of empirical evidence, the uncertainty rating in Table 1 is based on our study of available
literature and logical conjecture.
Table 1: Factors influencing Regulation and Policy Framework (R&PF) in the CLD.
Independent Variable
Dependent
Variable
Impact as Process
Uncertainty
Polarity
Sources
Policy Readiness
Compliance
enhancement
The essence of the policy decisions taken, both
nationally and globally, on how CAVs will be
accommodated and what type of vehicle
autonomy will be enabled is the most significant
impact on the need (or lack of need) for
infrastructure transformation in ITS. Policies are
typically the driving force behind the legislation.
Clear and concise policies for CAVs operation
will facilitate cyber-safe CAV-supported ITS.
High
+
(Geels and
Penna, 2015,
Taeihagh and
Lim, 2019,
Johnson,
2017).
Regulation/Laws
Readiness
Compliance
enhancement
Given the ubiquitous nature of cyber-attacks,
hacktivists tend to seek out and exploit legal
loopholes. The operating guidelines should
cover insurance procedures, crash guidelines,
acceptable autonomous vehicle ethical conduct,
data storage and tracking of communication
data, e.g., Licensing and permits-issuing of a
CAV user license after CAV safety education.
High
+
(Seuwou et
al., 2020,
Hodge et al.,
2019, Liu et
al., 2020).
Liability Readiness
Compliance
enhancement
A supporting factor for enhancing compliance is
defining a clear liability.For example, in the
event of a damage-causing incident (such as the
Tesla accident), the liability that regulates the
law must be clearly defined. The liability
requirements are likely to be settled
incrementally on distinctly restricted grounds
through legal precedent on navigation and crash
avoidance systems in the near term.
High
+
(Rosique et
al., 2019,
Lederman et
al., 2016)
CAVs Criminology-
Theory Maturity
Compliance
enhancement
Criminal design is often an afterthought in
developing new technology and is rarely
considered from the start of innovation of new
technology, as is the case with CAVs
technology. The pervasiveness of CAV
connectivity broadens the scope of crimes
committed in both cyberspace and physical
space in ITS. The criminological theory aids in
comprehending CAVs cyber-crimes and
criminal justice, thereby improving CAVs
compliance enhancement.
High
+
(Newton,
2017, Elzen et
al., 2004).
eSafety Traffic Unit
Compliance
enhancement
The nature of CAV operation in ITS necessitates
the use of L&PF at all levels, from state to
national to worldwide. Therefore, new
authority, i.e. the esafety Traffic Unit, with pre-
existing expertise and new dynamic CAVs
operating knowledge, will make it easier to
combat cyber-physical space crimes. Similarly,
Medium
+
(Uzair, 2021)
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gaining a better understanding of the motivations
of CAVs hackers may contribute to the creation
of hacker countermeasures
Compliance
enhancement
Regulation and
Policy
Framework
(R&PF)
Robust CAVs regulatory laws and policy
framework are driven by integrating policy
readiness, regulation/laws readiness, liability
readiness, CAVs Criminology-Theory Maturity,
and eSafety Traffic Unit
High
+
(Khan et al.,
2020e, Feng
et al., 2020).
Regulation and Policy
Framework (R&PF)
Hacks defended
R&PF would have established policies and
strategies that would aid in the prevention of
cyber-attacks.
High
+
(Seuwou et
al., 2020, Liu
et al., 2020,
Khan et al.,
2021a).
Hacks defended
Successful
Cyber-Attacks
The high number of hacks being defended will
minimise the number of successful CAVs cyber-
attacks.
High
-
Regulation and Policy
Framework (R&PF)
Adoption
and
Induction
The regulatory and policy framework of CAVs
would impact a variety of dimensions, such as
privacy, confidentiality, operational safety,
short-term economic considerations, public
acceptance, ethical and legal issues.
Governments counter the perceived
cybersecurity threat of hackers (high successful
attacks) by enhancing the regulatory laws and
policy framework. Users then perceive that
action as creating barriers to their freedom. So,
users respond by decreasing imitation, which
slows CAVs adoption and induction.
Medium
-
(Seuwou et
al., 2020, He,
2018, Khan et
al., 2021a).
Adoption
(From Imitation and
Innovation)
CAVs Adopters
In terms of adaptation, the Bass Diffusion Model
(BDM) adequately explains CAVs penetration,
i.e., adoption by imitation and adoption by
innovation. Two essential parameters of BDM
are: i) the product's attractiveness and ii)
effectiveness of persuading potential adopters,
known as the coefficient of innovation.
Medium
+
(Bass, 1969,
Sterman,
2000).
Induction
(From Imitation and
Innovation)
CAVs Adopters
Induction includes public transit users, cyclists,
children and some elderly because of the
availability of CAV. The induced demand tends
to be a typical reaction to decongestion in car-
centric environments by public transit users.
Medium
+
(Mewton,
2005,
Williams et
al., 2020)
Successful cyber-
attacks
Adoption
and
Induction
Successful cyber-attacks will impact the
product's attractiveness and effectiveness of
persuading potential adopters, known as the
coefficient of innovation.
High
-
(Sterman,
2000).
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Successful cyber-
attacks
CAVs Log Files
Preservation
CAV's network observability is primarily based
on log files. All CAV activities in ITS are
documented in log files. Cyber-assaults -
valuable input to log file preservation - can serve
as lessons learned and aid in investigating hacker
attacks and motivations.
Medium
+
(Dimitriadis
et al., 2020)
CAVs Log Files
Preservation
CAVs
Technology
Readiness and
Maturity
Retaining log files for all CAVs interactions for
a specified period would improve ITS reliability,
protect CAVs cybersecurity posture of cloud
computing environments, and enhance CAVs
decision-making
Medium
+
(Prasad and
Rohokale,
2020)
CAVs Technology
Readiness and Maturity
(TRM)
CAVs
Communication
Cyber Safety
(CAVS-CCS)
TRM, which assesses the communication
framework of CAVs and demonstrates its
capabilities, is triggered by the level of
technology, procedures, qualified personnel and
information. Defence Science and Technology
Group in Australia spotlighted the nine-level of
estimating the maturity of technologies during
the acquisition phase. Innovation in technology
maturity will lead to more secured V2X
communication.
Medium
+
(Vimmerstedt
et al., 2015,
Australia-
Goverment,
2020, Khan et
al., 2021b).
CAVs Communication
Cyber Safety
(CAVs-CCS)
Hacks defended
A highly robust CAVs communication
framework is less vulnerable to attacks and is
incredibly difficult for hackers to infiltrate.
Additionally, Unnamed Aerial Vehicles may
function as an ad-hoc, cost-effective
telecommunications network, allowing CAVs to
communicate in mountainous or dark regions.
For instance, the use of UAVs in conjunction
with 5G networks, both for access and backhaul,
High
+
(Khan et al.,
2020d, Khan
et al., 2020c,
Khan et al.,
2020b, Khan
et al., 2020a,
Khan, 2020,
Khan, 2019,
Khan et al.,
2019)
Figure 1: The system architecture of the Causal Loop Diagram (CLD).
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4. Model Qualitative Analysis: Loops and System archetype.
CLDs coherently conceptualise dynamic systems to facilitate understanding of
interdependencies of R&PF in CAVs cybersecurity framework. The loops envision a "system
archetype" that disclose inherent limitations within the system by identifying intervention
opportunities, allowing policy recommendations to be developed appropriately (Sterman,
2000). The following sub-sections describe various feedback loops and system archetype.
4.1 Balancing Loop #1
Figure 2a depicts the balancing loop #1 in which policy readiness, regulation/laws readiness,
liability readiness, CAVs criminology-theory maturity, and the eSafety Traffic Unit reinforce
CAVs compliance enhancement. This loop describes the mechanism of how governments
combat the perceived cybersecurity threat posed by hackers by refining the R&PF and, as a
result, increasing the number of hacks defended.
4.2 Balancing Loop #2
Similarly, balancing loop #2 is shown in Figure 2b. Successful cyber-attacks are stored as
log files- a valuable input for CAVs TRM and CAVs-CCS. This, in turn, makes it possible for
a large number of successfully defended hacks. Alternatively, less knowledge of a hacker's
capability reduces the capacity to fight against hackers, resulting in a greater rate of successful
assaults.
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Figure 2: Feedback loops in CLD
a. Balancing loop #1
b. Balancing loop #2
c. Reinforcing loop #1
d. Reinforcing Loop #2
4.3 Reinforcing Loop #1
Figure 2c illustrates reinforcing loop #1. This loop explains how prospective CAV users view
the R&PF actions as impeding their freedom. As a result, users respond by decreasing
imitation, slowing the adoption and induction of CAVs adaptors and log file preservation.
Consequently, CAVs-CCS is hampered.
4.4 Reinforcing Loop #2
The reinforcing loop#2 is shown in Figure 2d. This loop demonstrates how successful
cyberattacks decrease CAVs adoption and induction. This may impede the CAVs TRM, CAVs-
CCS, as well as, hacks defended.
4.5 Holistic View: Shifting the burden-system archetype
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The loops envisage system archetype that reveals underlying system limits by highlighting
intervention prospects. Figure 3 depicts a holistic view and is analogous to the "shifting the
burden" archetype. Decision-makers in the "shifting the burden" archetype fail to find the
fundamental answer early on and are exposed to cumulative adverse effects as they turn to
short corrective measures. This explains the process by which governments respond to
perceived cybersecurity threats by strengthening R&PF; consumers and OEMs view this
activity as restricting their freedom.
Figure 3: Holistic view- "shifting the burden" system archetype
5. Discussions and policy recommendations
This article aims to illustrate the potential pitfalls of decision-making (R&PF) in governing
a complex and dynamic system, i.e., CAV-based ITS. An approach to dealing with a
complicated decision-making system is to recognise general structures referred to as systems
archetypes (Khan et al., 2022b). The idea underlying system archetypes is that undesirable
outcomes or side effects may be linked to common behavioural patterns. The development of
a CLD-based SD model for assessing the impact of R&PF on the cybersecurity of CAVs is a
significant contribution because it provides a dynamic, interconnected view of the "big
picture".
The balancing loops #1 and 2 illustrate how effective R&PF can thwart cyber-attacks with
the help of policy readiness, regulation/laws readiness, liability readiness, CAVs, criminology-
theory maturity, and the eSafety Traffic Unit. Reinforcing loops #1and 2, on the other hand,
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demonstrate how a change in one direction is exacerbated by further change. Decision-makers
should proactively identify these potential hazards: CLD-based SD models can assist in this
endeavour and provide an environment to simulate various decision situations. For instance,
what could a pitfall avoidance strategy in R&PF be that does not impair the freedom of CAV
users, investors and automakers?
The R&PF of CAVs would have an effect on a variety of aspects, including data security and
privacy, short-term economic considerations, public infrastructure, and social and economic
dimensions (He, 2018, Khan et al., 2020e, Seuwou et al., 2020). The complexity of the ITS
grows as more CAVs are deployed. While the R&PF for CAVs establishes safe operating limits
for CAVs, it is essential to formulate the R&PF with a measured and risk-adjusted approach to
avoid the repercussions described in reinforcing loops #1 and #2.
The most challenging matter in this context is determining the trade-off between the three
components: i) constraints on CAV users' privacy and freedom, ii) operational and data
accessibility limitations for CAV OEMs and service providers as well as protection of their
business investments, and iii) command and control limits for state regulators. Nevertheless,
some of the R&PF will be driven by real-world reasoning in courts, following the submission
of a typical CAV-related incident for adjudication.
6. Limitations and future extensions
Although the suggested model incorporates an in-depth, methodical, and rigorous approach,
it does have certain confines. Data scarcity, a high degree of uncertainty, and the subjectivity
nature of R&PF make the model's empirical assessment challenging. Therefore, the following
steps could be data collection for quantitative evaluation of the model. The primary source will
be a survey conducted with the appropriate field specialists, aided by pilot programmes such
as "Austroads Future Vehicles & Technology Program" in Australia (Austroads, 2021;
Vicroads, 2021). On the other hand, qualitative research may remain the dominant option for a
few years longer until an adequate amount of data becomes available.
7. Conclusions
This paper proposed a CLD-based SD model that incorporates key inter-disciplinary variables
and evaluates the impact of R&PF on CAVs cybersecurity in a dynamic and integrated manner.
Two loops are envisaged: "balancing loops" highlight instances where R&PF aid in preventing
cyber-attacks, and "reinforcing loops" reveal how imposing R&FP can offset its potential
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benefits by creating a detrimental parallel circle. Based on feedback loops, a "shifting the
burden" system archetype is proposed in which governments counter cyber-threats by boosting
R&PF while also decreasing CAVs adaptability through imitation and induction.
Recommendations for R&PF formulation include a balanced approach in the trade-off
between: i) constraints on CAV users' privacy and freedom, ii) operational and data
accessibility limitations for CAV OEMs and service providers, and iii) command and control
limits for state regulators.
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