Context-Aware Framework for Detecting False Data Injection Attacks in Connected and Automated Vehicles
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form.
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
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Project title: Context-Aware Framework for Detecting False Data Injection Attacks in Connected and Automated Vehicles
Project Lead: Dr. Fuad A. Ghaleb
Project ID: 16 - 45480630
Project description:
ehicular Ad Hoc Networks (VANETs) are a key enabler of road safety, traffic efficiency, and passenger comfort. They play a vital role in developing sustainable transportation systems and future smart cities by enabling Vehicle-to-Everything(V2X) communication and supporting Connected and Automated Vehicles (CAVs). A critical component of VANETs is the exchange of Cooperative Awareness Messages (CAMs), which allow vehicles to share real-time information such as speed, location, and heading. These messages extend a vehicle’s perception beyond its onboard sensors, forming the foundation of many safety and coordination systems in CAVs. However, the integrity of CAMs is vulnerable to a stealthy threats like False Data Injection (FDI) and spoofing attacks, where compromised or malicious vehicles send deceptive messages, potentially causing dangerous or even life-threatening scenarios. Vehicles may collude and transmit consistent but fraudulent messages, generating a deceptive perception of the environment. These attacks manipulate input data while maintaining normal system functionality, potentially leading to unsafe decisions without detection. These attacks manipulate input data while maintaining normal system functionality, potentially resulting in unsafe decisions that go undetected.
This project will focus on designing and developing a highly resilient and adaptive Context-Aware Detection Framework for False Data Injection Attack in CAVs to overcome the limitations of existing detection methods, which often rely on strong assumptions or are ineffective against insider threats and coordinated attacks.
The research will explore the integration of Data Fusion, Deep Learning, and Blockchain technologies to enhance detection accuracy and data integrity in dynamic, real-world environments. Data Fusion techniques will be employed to cross-verify CAM messages against a real-time environmental model constructed using verifier-independent physical sensors (e.g., cameras, radar, lidar, GPS). Deep Learning models, particularly neural architectures and large language models will be used for real-time analysis and validation of CAMs in complex, rapidly changing traffic scenarios. Blockchain technology will be leveraged to establish a tamper-proof, decentralized ledger for secure and verifiable message exchange among vehicles.
The research outcomes have the potential to significantly enhance the security of Cooperative Intelligent Transportation Systems (C-ITS) and improve the reliability and trustworthiness of future autonomous mobility networks.
Anticipated findings and contributions to knowledge:
The anticipated findings and contributions of this study can be summarized as follows:
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A novel context-aware adaptive approach for accurately detecting false data injection attacks in Connected and Automated Vehicles (CAVs), based on the integration of data fusion, deep learning, and blockchain technologies.
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False Data Injection Attack Modelling including sophisticated adversarial behaviours such as coordinated collusive attacks (or Botnets), to support rigorous simulation and evaluation of detection mechanisms.
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A real-time sensor-driven data validation model employing data fusion techniques and local heterogeneous sensor cross-validation to assess the consistency and plausibility of messages, utilizing the Kalman Filter algorithm and deep learning.
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A Byzantine Fault Tolerant (BFT) Temporal Ledger Consensus Algorithm to ensure the integrity, trustworthiness, and real-time validation of Cooperative Awareness Messages (CAMs), even in the presence of collusive or insider threats.
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An adaptive and decentralized detection architecture, robust against the unique challenges of highly dynamic, ephemeral vehicular networks, with applicability to future Cooperative Intelligent Transportation Systems (C-ITS) and smart city infrastructures.
Person Specification:
Essential Criteria:
- Academic Qualifications: A Bachelor's degree and a Master’s degree in Computer Science, Computer/Electrical Engineering, Cybersecurity, Networking and Security, Artificial Intelligence, or a closely related discipline.
- A strong understanding of computer networks, wireless communication, or vehicular communication systems.
- Strong understanding of cybersecurity principles, including authentication, data integrity, and cryptographic protocols.
- Solid foundation in machine learning, with hands-on experience implementing deep learning models.
- Proficiency in one or more programming languages commonly used in research and systems development (e.g., Python, C++, MATLAB).
- Demonstrated ability to think critically and solve complex technical problems independently.
- Good written and verbal communication skills, with the ability to write reports and present research findings clearly.
- A demonstrated interest in the research topic and the ability to work both independently and collaboratively as part of a research team.
Desirable Criteria:
- Prior experience in academic or industrial research (e.g., through a Master’s dissertation, undergraduate project, or internship) in areas such as network and cybersecurity or AI for sensor data analysis.
- Evidence of scholarly activity such as authored papers, conference presentations, or technical documentation.
- Excellent software development skills.
- Understanding of connected and autonomous vehicle security, vulnerability analysis, and cybersecurity.
- Familiarity with data fusion techniques and sensor integration
- Basic understanding of, or practical experience with, distributed ledger technologies, particularly in secure communications or IoT systems.
- Experience with network or traffic simulation platforms (e.g., NS-3, SUMO, Veins).
- Willingness to learn new tools, technologies, and research methods as the project evolves.
- Experience with network or traffic simulation platforms (e.g., NS-3, SUMO, Veins).
- Willingness to learn new tools, technologies, and research methods as the project evolves.
Contact:
- For enquiries about the funding or project proposal, please contact: fuad.ghaleb@bcu.ac.uk.
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk