All Outputs (4)

Assuring privacy of AI-powered community driven Android code vulnerability detection. (2025)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., PIRAS, L., AL-KADRI, M.O. and PETROVSKI, A. 2025. Assuring privacy of AI-powered community driven Android code vulnerability detection. In Garcia-Alfaro, J., Kalutarage, H., Yanai, N. et al. (eds.) Computer security: ESORICS 2024 international workshops: revised selected papers from the proceedings of eleven international workshops held in conjunction with the 29th European Symposium on Research in Computer Security (ESORICS 2024), 16-20 September 2024, Bydgoszcz, Poland. Part II. Lecture notes in computer science, 15264. Cham: Springer [online], pages 457-476. Available from: https://doi.org/10.1007/978-3-031-82362-6_27

The challenge of training AI models is heightened by the limited availability of data, particularly when public datasets are insufficient. While obtaining data from private sources may seem like a viable solution, privacy concerns often prevent data... Read More about Assuring privacy of AI-powered community driven Android code vulnerability detection..

Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach. (2024)
Thesis
SENANAYAKE, J.M.D. 2024. Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801183

As technology advances, Android devices and apps are rapidly increasing. It is crucial to adhere to security protocols during app development, especially as many apps lack sufficient safeguards. Despite the use of automated tools for risk mitigation,... Read More about Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach..

Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security. (2024)
Thesis
RAJAPAKSHA, S. 2024. Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801124

Modern automobiles are equipped with a large number of electronic control units (ECUs), which are interconnected through the controller area network (CAN) bus for real-time data exchange. However, the CAN bus lacks security measures, rendering it sus... Read More about Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security..

Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. (2024)
Thesis
OTOKWALA, U.J. 2024. Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2571244

Critical Infrastructures (CI) are essential for various aspects of human activities, spanning across different sectors. However, the integration of Internet of Things (IoT) devices into CI has introduced a new dimension to security challenges due to... Read More about Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures..