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Challenges of Securing Artificial Intelligence-Powered Systems from

Cyber Threats: Case Study of Autonomous Vehicles

Oluwatosin Ogunlade

Abimbola Ogunlade*

Mobolaji Tenibiaje.

University of East London, UK

Engineering Institute of Technology, Australia

Bamidele Olumilua University of Education, Science and Technology Ikere Ekiti.

*Corresponding Author

DOI : https://doi.org/10.51583/IJLTEMAS.2025.1402007

Received: 18 February 2025; Accepted: 22 February 2025; Published: 07 March 2025

Abstract: The integration of Artificial intelligence (AI) into various sectors, including transportation, has a significant impact on human

endeavors, in addition to eco-friendly advantages. One of the most promising areas of AI-powered systems is the manufacture of

Autonomous Vehicles (AVs). These self-driving cars, also known as driverless, are intelligent vehicles that can operate without human

aid or support. AVs are equipped with sophisticated AI-powered technologies such as sensors, radars, Global Positioning System (GPS),

and advanced algorithms that can transmit information and navigate the environment using analyzed data. These driverless cars have the

potential of revolutionizing the transport sector by improving efficiency, reducing road accidents, improving flexibility, and decreasing

congestion. However, AI in AV applications poses some risks and challenges associated with securing systems from cybersecurity threats

and attacks. This paper explores the dangers and difficulties of securing AI systems from cyber threats, highlighting various detection and

prevention mechanisms. The ethical and legal implications, including strategies to address these challenges proactively, are also

discussed. It is believed that the challenges in the automotive industry can be mitigated through collaboration among stakeholders,

manufacturers, researchers, IT professionals, and policymakers by implementing robust security measures, conducting regular

vulnerability assessments, and leveraging the expertise of software security specialists. Collaboration between industry and cybersecurity

professionals is essential to safeguarding public safety.

Keywords: Autonomous Vehicles, Artificial Intelligence, Transportation, Cyber Threats.

I. Introduction

The integration of Artificial Intelligence (AI) into various sectors, including transportation, has had a profound impact on human

activities, offering efficiency, automation, and sustainability benefits. One of the most significant applications of AI is in Autonomous

Vehicles (AVs), also known as self-driving or driverless cars. These vehicles operate without direct human assistance; these systems

leverage sensors, radars, cameras, Global Positioning Systems (GPS), and advanced AI-driven algorithms to perceive and navigate their

environment effortlessly (Scalas & Giacinto, 2019).

Autonomous vehicles have revolutionized the transportation industry by reducing road accidents, easing traffic congestion, which is

considered human-induced, improving mobility, and enhancing energy efficiency (Kojchev et al., 2022; Wang et al., 2021). However,

despite these benefits, AVs present serious cybersecurity concerns that could impact their functionality, safety, and public acceptance. As

AI-powered transportation systems become increasingly connected, they also become more vulnerable to cyber threats, including remote

hacking, data breaches, and system manipulation (Boddupalli et al., 2022; Khattak et al., 2021). A successful cyber-attack on an AV could

compromise its control system, leading to severe accidents, privacy violations, and operational failures.

This research aims to analyze the risks and challenges of securing AI-powered autonomous vehicle systems from cyber threats and

attacks. It explores various cybersecurity vulnerabilities, risk mitigation strategies, and best practices for enhancing the security of these

intelligent transportation systems. Furthermore, the study highlights detection and prevention mechanisms and discusses the ethical and

legal implications of securing AVs. It is believed that effective collaboration among stakeholders, manufacturers, researchers, IT

professionals, and policymakers is crucial for addressing these challenges. By implementing robust security frameworks, regular

vulnerability assessments, and adopting advanced AI security models, AVs can be safeguarded against evolving cyber threats, ensuring

their safe and reliable integration into modern transportation ecosystems.

II. Background

The concept of Artificial Intelligence (AI) was first introduced in 1956 at a Dartmouth College conference, where researchers such as

John McCarthy, Allen Newell, Herbert Simon, and Marvin Minsky explored the possibility of creating "thinking machines" capable of

simulating human intelligence. AI is a branch of computer science that focuses on developing intelligent systems capable of performing

tasks that typically require human cognition, such as decision-making, language processing, and pattern recognition.

Over the decades, AI has evolved into a powerful technology integrated into various aspects of daily life, including virtual assistants,

social media algorithms, facial recognition, speech recognition, and autonomous systems. Its impact spans multiple industries, such as

healthcare, finance, retail, and transportation. One of the most promising applications of AI is in Autonomous Vehicles (AVs), where AI-

driven systems allow self-driving cars to perceive their surroundings, analyze data, and make real-time navigation decisions.

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AI-powered AVs utilize a combination of machine learning, deep learning, and neural networks to process large amounts of data from

sensors and cameras. This enables them to detect obstacles, recognize road signs, and predict the movement of pedestrians and other

vehicles. These capabilities enhance driving safety, efficiency, and the overall road experience. However, the increasing dependence on

AI in AVs also raises concerns regarding security, as cybercriminals can exploit vulnerabilities in AI-driven systems to manipulate

vehicle controls, steal sensitive data, or disrupt operations.

The rapid development of AI-powered technologies underscores the need for robust security measures, ethical considerations, and

regulatory frameworks to ensure their safe and reliable deployment. As AI continues to advance, ongoing research is essential to address

cybersecurity threats and optimize its applications across various sectors, particularly in transportation.

Definition and Components of AI-Powered Autonomous Vehicles

Autonomous vehicles, or self-driving cars, are defined by their ability to function without human interference, utilizing AI procedures and

sensors to observe the environment, make decisions, and navigate (Alrubaian et al., 2019). These vehicles rely on a combination of

hardware and software components, including proximity sensors, cameras, GPS, radar, advanced automation systems, actuators,

processors, algorithms, and communication systems. The integration of these technologies enables AVs to detect objects, navigate

through traffic, and safely reach their destinations. Augmented reality further enhances the driving experience by displaying vital

information to drivers in innovative ways.

Overview of AI-Powered Autonomous Vehicles

Autonomous Vehicles (AVs), also known as self-driving cars, have become a significant focus in the transportation sector due to

advancements in Artificial Intelligence (AI). These vehicles integrate sophisticated technologies such as sensors, cameras, radars, and the

Global Positioning System (GPS) to perceive and navigate their surroundings without human intervention (Alrubaian et al., 2019).

Companies like Tesla, Google, and Mercedes-Benz are leading the charge in developing AVs, aiming to enhance road safety, reduce

traffic congestion, and improve mobility for individuals with disabilities (Zamindar, 2022).

Potential Benefits and Challenges

The deployment of AVs presents numerous advantages, including enhanced road safety, reduced environmental impact, and optimized

traffic flow. According to Sadiku et al. (2021), AI in AVs improves efficiency by automating driving decisions, thereby minimizing

human errors related to fatigue, distraction, and impaired judgment. AVs can also predict mechanical failures and schedule proactive

maintenance, reducing the likelihood of breakdowns (Atakishiyev et al., 2023).

Despite these benefits, AVs face substantial challenges, particularly in cybersecurity. The reliance on wireless networks and

interconnected systems makes them vulnerable to cyber threats such as hacking, data breaches, and system manipulation (Aurangzeb et

al., 2023). The lack of universal safety standards and regulatory frameworks further complicates the widespread adoption of AVs.

Additionally, the high development and maintenance costs remain a barrier to large-scale implementation (Algarni & Thayananthan,

2022).

Overview of Existing Research on Cyber Threats to AVs

Cybersecurity threats in AVs have been well-documented, with researchers identifying multiple vulnerabilities in autonomous driving

systems. One of the most notable incidents was the remote hacking of a Jeep Cherokee in 2015, where attackers exploited weaknesses in

the vehicle's infotainment system to gain full control (Henze et al., 2018). Similarly, Greenberg (2018) documented the NotPetya

cyberattack, which affected critical infrastructure, highlighting the potential risks to AV networks.

A study by Meissner (2019) identified key cyber threats to AVs, including GPS spoofing, malware injections, denial-of-service (DoS)

attacks, and unauthorized remote access. These threats can compromise vehicle functionality, endanger passenger safety, and disrupt

transportation systems. Radoglou-Grammatikis et al. (2018) emphasized that AVs' continuous reliance on over-the-air software updates

(SOTA and FOTA) increases their exposure to cyber risks. Attackers can exploit these updates to introduce malicious code into AV

systems.

Autonomous Vehicle Cybersecurity Risks and Challenges

The cybersecurity landscape for AVs is rapidly evolving, with new attack vectors emerging as technology advances. One major concern is

data privacy, as AVs collect and process vast amounts of sensitive user information, including travel patterns, personal preferences, and

biometric data (Savitha & Madhu, 2023). Unauthorized access to this data can lead to identity theft, financial fraud, and surveillance risks.

Another significant risk is sensor manipulation, where attackers interfere with AV sensors to distort environmental perception. Alrajeh &

Prenosil (2019) demonstrated that hackers could alter LiDAR and camera readings, causing AVs to misinterpret road conditions and make

hazardous driving decisions. Additionally, supply chain vulnerabilities pose a critical threat, as compromised hardware or software

components from third-party vendors can introduce backdoors into AV systems (Kukkala et al., 2020).

Attack vector

Impact

Consequences

Vehicle's entertainment

system

Hijack and locking of the in-vehicle entertainment.

The vehicle occupant was unable to turn on

the entertainment system.

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Control System

Hijack and misuse of the vehicle features such as the in-

vehicle audio system and arbitrarily increasing the volume.

The distraction of the driver

Car locking system

Locking vehicle resulting in jack ware

Denial of access into the vehicle.

Externally connected devices

The unauthorized modification of

vehicle files and access to connected devices.

Compromise of integrity of the car files and

denial of service of

user-brought-in devices connected to the car

Computational

resources of the vehicle

Arbitrary consumption of

computational resources (such as memory and CPU cycles)

Disruption of vehicle operations

Sensitive and private data

compromise

Unauthorized access to personal and sensitive data of car

users.

Theft of users' confidential data

Vehicle safety system

Disabling vehicle safety functions

Compromise of passenger safety

Compromised Connected

Autonomous Vehicle (CAV)

Using a compromised vehicle to send misleading, false, and

bogus data to other CAVs

Impersonation of AV, thereby leading to the

exchange of compromised information.

Recommendations for Securing Autonomous Vehicle Systems

To mitigate cybersecurity risks, researchers and industry professionals have proposed several defensive strategies. McKeever et al. (2020)

advocate for multi-layered security architectures that incorporate encryption, intrusion detection systems (IDS), and AI-based anomaly

detection. The adoption of zero-trust security models, where every system interaction requires continuous authentication, is also

recommended (Miettinen & Gasser, 2017).

Furthermore, regulatory bodies such as the International Organization for Standardization (ISO) and the Society of Automotive Engineers

(SAE) have introduced standards like ISO/SAE 21434, which outlines cybersecurity requirements for AVs (Kshetri, 2018). Regular

penetration testing, security audits, and collaboration between automotive manufacturers and cybersecurity experts are essential for

strengthening AV defenses (Antonini et al., 2020).

The increasing reliance on Artificial Intelligence (AI) in Autonomous Vehicles (AVs) has introduced significant cybersecurity challenges,

making it essential to develop robust security measures. A comprehensive security framework must incorporate multi-layered defenses,

encryption technologies, regulatory compliance, and continuous monitoring to mitigate cyber threats effectively. Researchers and industry

professionals have proposed various strategies to strengthen AV security and protect them from malicious actors.

1. Implement Cybersecurity-Conscious Design Techniques

One of the critical shortcomings in AV security is that cybersecurity is often treated as an afterthought rather than an integral part of the

design process (Horowitz & Lucero, 2017; Khan et al., 2020) (Antonini et al., 2020). To address this issue, the security-by-design

approach must be adopted, integrating security measures into the Software Development Lifecycle (SDL) from the early stages. A

defense-in-depth model is also essential, ensuring that multiple layers of security mechanisms protect vehicle systems (Ansari et al., 2018;

Kukkala et al., 2022).

2. Secure the Software and Hardware Stack

Modern AVs rely on Electronic Control Units (ECUs) to manage key functions such as steering, acceleration, and braking. These ECUs

are attractive targets for cybercriminals, making it crucial to implement robust security measures (Ayres et al., 2021; Yousseef et al.,

2024). One way to protect ECUs is through the use of Hardware Security Modules (HSMs) and Trusted Platform Modules (TPMs), which

provide secure key storage and cryptographic capabilities (Henze et al., 2018).

Additionally, software-over-the-air (SOTA) and firmware-over-the-air (FOTA) updates should be implemented to ensure that AVs

receive security patches regularly. While many manufacturers have adopted SOTA updates, FOTA is still underutilized, exposing AVs to

vulnerabilities (Ayres et al., 2021; Catuogno & Galdi, 2023; Radoglou-Grammatikis et al., 2018). Ensuring that these updates are digitally

signed and verified before installation will help prevent cybercriminals from injecting malicious code into AV systems (Kukkala et al.,

2020).

3. Strengthen Communication Security

AVs rely heavily on wireless communication for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interactions. However,

these communication channels can be exploited by cybercriminals to intercept or manipulate data (Boddupalli et al., 2022). Researchers

suggest using end-to-end encryption and authentication protocols such as Transport Layer Security (TLS) and Public Key Infrastructure

(PKI) to secure AV communications (Miettinen & Gasser, 2017).

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Another effective technique is intrusion detection and prevention systems (IDPS), which monitor network traffic and detect anomalies

that could indicate a cyberattack (Meissner, 2019). Additionally, anomaly-based AI detection systems can identify and respond to unusual

behavior in real time, minimizing the risk of AV hijacking (Savitha & Madhu, 2023).

4. Develop Industry-Wide Security Standards and Regulations

A significant challenge in AV cybersecurity is the lack of universal security regulations. Although ISO and SAE have introduced

ISO/SAE 21434, which provides cybersecurity guidelines for AV manufacturers, there is still a need for globally standardized policies

(Kshetri, 2018).

Governments and regulatory bodies must enforce strict compliance measures, including:

 Regular security audits and penetration testing for AV software (Alrajeh & Prenosil, 2019).

 Implementing real-time cybersecurity monitoring centers to track and respond to cyber threats targeting AV fleets (Algarni &

Thayananthan, 2022).

 Establishing legal accountability for cybersecurity breaches to ensure manufacturers prioritize security investments (Atakishiyev et

al., 2023).

5. Enhance Supply Chain Security

AVs are complex systems with components sourced from multiple third-party vendors. This supply chain dependency introduces

significant cybersecurity risks, as attackers can exploit vulnerabilities in software or hardware supplied by external manufacturers

(Greenberg, 2018). To mitigate these risks, companies should:

 Conduct rigorous security assessments of all suppliers.

 Require third-party vendors to comply with secure coding practices and software verification standards (Aurangzeb et al., 2023).

 Use blockchain technology for tracking the authenticity of AV components and preventing counterfeit parts from being introduced

into the supply chain (Kukkala et al., 2020).

6. Leverage Artificial Intelligence for Threat Detection

AI-based security mechanisms can significantly improve AV cybersecurity by identifying suspicious behavior and potential cyberattacks

in real-time. Machine learning algorithms can analyze large volumes of data and detect patterns indicative of an attack before it occurs

(Meissner, 2019).

Deep learning-based security models can also help distinguish between legitimate and fraudulent firmware updates, ensuring that AVs do

not install malicious software disguised as system patches (Radoglou-Grammatikis et al., 2018).

7. Conduct Regular Cybersecurity Training and Awareness Programs

Human error remains one of the most significant vulnerabilities in cybersecurity. Even with robust technical defenses, AV systems remain

at risk if stakeholders—including manufacturers, software engineers, fleet operators, and end-users—are not adequately trained in

cybersecurity best practices (McKeever et al., 2020).

Organizations should implement mandatory cybersecurity training for all personnel involved in AV development and maintenance.

Awareness programs should also educate vehicle owners on best practices, such as using strong authentication measures and regularly

updating their vehicle's software (Savitha & Madhu, 2023).

8. Establish Cybersecurity Incident Response Plans

Despite the best preventive measures, cyberattacks on AVs are inevitable. Therefore, manufacturers and regulatory agencies must develop

incident response plans to minimize damage and ensure quick recovery from cyber incidents (Kshetri, 2018).

A robust incident response strategy should include:

 Real-time monitoring to detect cyber threats as they emerge.

 Automated rollback features that restore compromised AV software to its last secure state (Henze et al., 2018).

 Collaboration with law enforcement and cybersecurity agencies to track and neutralize cybercriminal activities (Aurangzeb et al.,

2023).

Roadmap elements

Components

Cybersecurity-aware design practices

Defense-in-depth / multi-layered security

Zero-trust security approach

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Security requirements

Secure the software and hardware stack.

HSMs/TPMs

SDLs

SOTA/FOTA updates

New Standards and Regulations for

ISO/SAE 21434

Automotive Security and AI.

Rules and regulations

Intelligence on advanced threats

Penetrations tests

Vulnerability assessments

Key elements in the cybersecurity roadmap for autonomous vehicles.

III. Research Methodology, Methods, and Ethical Considerations

Research Approach and Design

The research employs a case study approach to examine the risks and challenges of securing AI-powered Autonomous Vehicles (AVs)

from cyber threats. A case study methodology provides an in-depth investigation of real-world cybersecurity incidents, vulnerabilities,

and countermeasures in AV systems. This approach enables a comprehensive analysis of the problem while considering multiple

perspectives from industry reports, academic literature, and existing security frameworks (McKeever et al., 2020).

The study is qualitative in nature, utilizing secondary data sources such as peer-reviewed journal articles, conference papers, technical

reports, and cybersecurity threat analyses. These sources offer rich contextual insights into AV cybersecurity challenges and best practices

for mitigating risks. (Meissner, 2019).

A structured content analysis method is applied to identify recurring cybersecurity themes, trends, and vulnerabilities in AV systems. This

involves systematically reviewing academic and industry sources to extract relevant data on security risks, attack vectors, and proposed

solutions (Kukkala et al., 2020).

Additionally, comparative analysis is used to evaluate different cybersecurity frameworks and regulations, such as ISO/SAE 21434,

which outlines cybersecurity risk management for AVs (Kshetri, 2018). The study also references historical case studies of cyberattacks

on AVs, such as the Jeep Cherokee hack (2015) and Tesla’s system vulnerabilities, to highlight real-world security breaches and their

implications (Greenberg, 2018).

Data Collection and Analysis

Since the study is non-experimental, data is obtained exclusively from secondary sources. The data collection process involves gathering

cybersecurity reports, regulatory guidelines, research papers, and industry case studies (Alrajeh & Prenosil, 2019).

The data analysis phase follows a thematic approach, where relevant cybersecurity threats, countermeasures, and industry trends are

identified and categorized (Radoglou-Grammatikis et al., 2018). Content from multiple sources is synthesized to highlight commonalities

in AV cybersecurity risks and identify gaps in existing security solutions.

The key steps in the data analysis process include:

1. Identification of recurring cybersecurity threats (e.g., GPS spoofing, malware, sensor manipulation).

2. Categorization of cyber threats by severity and impact on AV operations (Savitha & Madhu, 2023).

3. Comparative evaluation of cybersecurity solutions (e.g., encryption, intrusion detection, AI-based anomaly detection)

(Aurangzeb et al., 2023).

4. Analysis of regulatory compliance and security best practices across different AV manufacturers (Algarni & Thayananthan,

2022).

Limitations of the Adopted Research Approach

While the case study method provides valuable insights into AV cybersecurity risks, it has certain limitations. One major challenge is the

lack of direct empirical data since the study relies on secondary sources. Unlike experimental research, this approach does not involve

hands-on testing of AV security vulnerabilities (Meissner, 2019).

Another limitation is potential data bias in the sources used. Cybersecurity research is often influenced by industry perspectives, which

may overemphasize certain risks while downplaying others (McKeever et al., 2020). To mitigate this issue, the study incorporates a

diverse range of sources, including independent academic research, cybersecurity white papers, and regulatory reports.

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Additionally, the rapidly evolving nature of AV cybersecurity threats means that findings may become outdated as new attack techniques

emerge. Continuous monitoring and adaptation of cybersecurity frameworks are necessary to address these evolving challenges (Kukkala

et al., 2020).

Ethical Considerations Relevant to the Research

Since this study involves secondary data analysis, ethical considerations focus on data integrity, source credibility, and responsible use of

information (Kshetri, 2018). Key ethical principles guiding the research include:

1. Citation and Avoidance of Plagiarism

Proper attribution is given to all academic sources, industry reports, and regulatory guidelines referenced in the study (Alrajeh & Prenosil,

2019). This ensures compliance with academic integrity standards and prevents the misrepresentation of research findings.

2. Selection of Credible and Reliable Sources

The study prioritizes peer-reviewed journal articles, government reports, and cybersecurity standards over non-verified online sources.

The credibility of data is assessed based on author qualifications, publication source, and citation frequency (Radoglou-Grammatikis et

al., 2018).

3. Ethical Use of Findings

The research findings are used solely for academic purposes, with no intent to exploit or misrepresent data. Ethical handling of

cybersecurity-related information is crucial to prevent misuse by malicious actors (Savitha & Madhu, 2023).

4. Data Privacy and Confidentiality

Although no personally identifiable information (PII) is used, the study adheres to data privacy standards, ensuring that all referenced

materials comply with General Data Protection Regulation (GDPR) and other data protection laws (Algarni & Thayananthan, 2022).

5. Research Validity and Reliability

To ensure research validity, the study employs triangulation, where findings from multiple sources are cross-verified to enhance accuracy

(Meissner, 2019). The study also maintains transparency in data collection by documenting the selection criteria for research materials

(Kukkala et al., 2020).

Reliability is ensured by using standardized analysis methods, such as content analysis and comparative evaluation of cybersecurity

frameworks (Aurangzeb et al., 2023). The findings are structured to allow replicability, enabling future researchers to build upon the

study’s insights.

IV. Conclusion

Artificial intelligence is advancing rapidly in the automotive industry as the backbone of self-driving vehicles. Securing AI-powered

systems, particularly autonomous vehicles, from cyber threats is a complex and evolving challenge. This report highlights the risks

associated with cybersecurity in autonomous vehicles and the various detection and prevention mechanisms researchers propose. Ethical

and legal implications, industry initiatives, and best practices are also discussed. Further research and collaboration among academia,

industry, and policymakers are crucial to address the evolving cybersecurity landscape and ensure the safe deployment of AI-powered

systems in the transportation sector.

It is equally essential for stakeholders in the automotive industry, including manufacturers, researchers, policymakers, and cybersecurity

professionals, to address these risks proactively. Implementing robust security measures, conducting regular vulnerability assessments,

promoting secure software development practices, and fostering collaboration between industry and cybersecurity experts are crucial

steps to mitigate these risks and ensure the safe and secure adoption of autonomous vehicles. Examples of how cybercriminals have

already demonstrated their intent by exploiting several vulnerabilities in the automotive ecosystem's intelligent transport systems can be

found with the development of technology and the use of innovative connected vehicles. We are going to see a massive increase in cyber

attacks on them. In contrast to malware that may be present on people's computers and mobile devices, the vulnerabilities of AV software

could pose far more danger.

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