All Outputs (10)

Assessing security vulnerabilities in Sri Lankan banking mobile applications: challenges and solutions. (2025)
Presentation / Conference Contribution
RAVICHANDRAN, L., PIYUMANTHA, K., WICKRAMASINGHE, W.S., WEERASINGHE, M. and SENANAYAKE, J. 2025. Assessing security vulnerabilities in Sri Lankan banking mobile applications: challenges and solutions. In Proceedings of the 8th International research conference on Smart computing and systems Engineering 2025 (SCSE 2025), 3 April 2025, Colombo, Sri Lanka. Piscataway: IEEE [online], pages 1-6. Available from: https://doi.org/10.1109/SCSE65633.2025.11031031

Mobile banking plays a crucial role in Sri Lanka's financial sector, offering convenience through self-service technologies. Despite its rapid adoption, concerns about security continue to affect customer trust, underscoring the critical need for enh... Read More about Assessing security vulnerabilities in Sri Lankan banking mobile applications: challenges and solutions..

DroidKey: a practical framework and analysis tool for API key security in Android applications. (2025)
Presentation / Conference Contribution
PIYUMANTHA, K., SENANAYAKE, J. and WIJAYASIRIWARDHNE, K. 2025. DroidKey: a practical framework and analysis tool for API key security in android applications. In Proceedings of the 8th International research conference on Smart computing and systems Engineering 2025 (SCSE 2025), 3 April 2025, Colombo, Sri Lanka. Piscataway: IEEE [online], pages 1-6. Available from: https://doi.org/10.1109/SCSE65633.2025.11030956

The reliance on mobile applications has amplified concerns about Application Programming Interface (API) key security in Android platforms. Serving as essential authentication mechanisms, API keys ensure secure communication with external services. H... Read More about DroidKey: a practical framework and analysis tool for API key security in Android applications..

Advanced DDoS attack detection and mitigation in software-defined networking (SDN) environments: an integrated machine learning approach. (2025)
Presentation / Conference Contribution
GAYANTHA, N., RAJAPAKSE, C. and SENANAYAKE, J. 2025. Advanced DDoS attack detection and mitigation in software-defined networking (SDN) evironments: an integrated machine learning approach. In Proceedings of the 8th International research conference on Smart computing and systems Engineering 2025 (SCSE 2025), 3 April 2025, Colombo, Sri Lanka. Piscataway: IEEE [online], pages 1-6. Available from: https://doi.org/10.1109/SCSE65633.2025.11030982

The increasing sophistication of Distributed Denial of Service (DDoS) attacks poses critical challenges to network security, necessitating advanced detection and mitigation strategies. This research presents a machine learning-based framework that ef... Read More about Advanced DDoS attack detection and mitigation in software-defined networking (SDN) environments: an integrated machine learning approach..

Integrating large language models for automated vulnerability scanning and reporting in network hosts. (2025)
Presentation / Conference Contribution
SANDARUWAN, M.T., WIJAYANAYAKE, J. and SENANAYAKE, J. 2025. Integrating large language models for automated vulnerability scanning and reporting in network hosts. In Proceedings of the 8th International research conference on Smart computing and systems Engineering 2025 (SCSE 2025), 3 April 2025, Colombo, Sri Lanka. Piscataway: IEEE [online], pages 1-7. Available from: https://doi.org/10.1109/SCSE65633.2025.11031059

This research explores integrating Large Language Models (LLMs) like GPT-4 and Claude 3.5 into cybersecurity vulnerability scanning to enhance automation and effectiveness. Current tools' reliance on manual updates and human expertise is highlighted.... Read More about Integrating large language models for automated vulnerability scanning and reporting in network hosts..

Enhancing network intrusion detection with stacked deep and reinforcement learning models. (2025)
Presentation / Conference Contribution
KALPANI, N., RODRIGO, N., SENEVIRATNE, D., ARIYADASA, S. and SENANAYAKE, J. 2025. Enhancing network intrusion detection with stacked deep and reinforcement learning models. In Proceedings of the 8th International research conference on Smart computing and systems Engineering 2025 (SCSE 2025), 3 April 2025, Colombo, Sri Lanka. Piscataway: IEEE [online], pages 1-7. Available from: https://doi.org/10.1109/SCSE65633.2025.11031023

This study investigates the effectiveness of Ensemble Learning (EL) techniques by integrating reproducible Deep Learning (DL) and Reinforcement Learning (RL) models to enhance network intrusion detection. Through a systematic review of the literature... Read More about Enhancing network intrusion detection with stacked deep and reinforcement learning models..

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..

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..

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 5th 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..

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..