All Outputs (5)

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..

Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques. (2025)
Journal Article
KALPANI, N., RODRIGO, N., SENEVIRATNE, D., ARIYADASA, S. and SENANAYAKE, J. 2025. Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques. Iran journal of computer science [online], Online First. Available from: https://doi.org/10.1007/s42044-025-00246-8

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments... Read More about Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques..

Customizable DDoS attack data generation in SDN environments for enhanced machine learning detection models. (2025)
Presentation / Conference Contribution
GAYANTHA, N., RAJAPAKSE, C. and SENANAYAKE, J. 2025. Customizable DDoS attack data generation in SDN environments for enhanced machine learning detection models. In Proceedings of the 25th International conference on advanced research in computing 2025 (ICARC 2025): converging horizons: uniting disciplines in computing research through AI innovation, 19-20 February 2025, Belihuloya, Sri Lanka. Piscataway: IEEE [online], pages 386-391. Available from: https://doi.org/10.1109/icarc64760.2025.10963190

Distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of Software-Defined Networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup '99 and CICIDS 2017, are... Read More about Customizable DDoS attack data generation in SDN environments for enhanced machine learning detection models..

DevSecOps implementation for continuous security in financial trading software application development. (2025)
Presentation / Conference Contribution
DASANAYAKE, S.D.L.V., SENANAYAKE, J. and WIJAYANAYAKE, W.M.J.I. 2025. DevSecOps implementation for continuous security in financial trading software application development. In Proceedings of the 25th International conference on advanced research in computing 2025 (ICARC 2025): converging horizons: uniting disciplines in computing research through AI innovation, 19-20 February 2025, Belihuloya, Sri Lanka. Piscataway: IEEE [online], pages 457-462. Available from: https://doi.org/10.1109/ICARC64760.2025.10963292

DevSecOps incorporates security into the DevOps workflow, ensuring robust protection throughout the software development lifecycle. This research addresses the security gaps in financial trading applications, where traditional methods often prioritiz... Read More about DevSecOps implementation for continuous security in financial trading software application development..

MADONNA: browser-based malicious domain detection using optimized neural network by leveraging AI and feature analysis. (2025)
Journal Article
SENANAYAKE, J., RAJAPAKSHA, S., YANAI, N., KALUTARAGE, H. and KOMIYA, C. 2025. MADONNA: browser-based malicious domain detection using optimized neural network by leveraging AI and feature analysis. Computers and security [online], 152, article number 104371. Available from: https://doi.org/10.1016/j.cose.2025.104371

Detecting malicious domains is a critical aspect of cybersecurity, with recent advancements leveraging Artificial Intelligence (AI) to enhance accuracy and speed. However, existing browser-based solutions often struggle to achieve both high accuracy... Read More about MADONNA: browser-based malicious domain detection using optimized neural network by leveraging AI and feature analysis..