All Outputs (57)

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..

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..

Improving federated learning performance with similarity guided feature extraction and pruning. (2024)
Thesis
PALIHAWADANA, C. 2024. Improving federated learning performance with similarity guided feature extraction and pruning. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801100

Federated Learning (FL) is a Machine Learning (ML) paradigm that learns from distributed clients to collaboratively train a global model in a privacy-preserved manner without sharing their private data. Traditional centralised ML approaches require a... Read More about Improving federated learning performance with similarity guided feature extraction and pruning..

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..

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. (2024)
Presentation / Conference Contribution
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]. Available from: https://doi.org/10.1007/978-3-031-82362-6

This two-volume set LNCS 15263 and LNCS 15264 constitutes the refereed proceedings of eight International Workshops which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024, in Bydgoszcz, Poland,... Read More about 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..

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..

Cross-validation for detecting label poisoning attacks: a study on random forest algorithm. (2024)
Presentation / Conference Contribution
YASARATHNA, T.L., MUNASINGHE, L., KALUTARAGE, H. and LE-KHAC, N.-A. 2024. Cross-validation for detecting label poisoning attacks: a study on random forest algorithm. In Pitropakis, N., Katsikas, S., Furnell, S. and Markantonakis, K. (eds.) Proceedings of the 39th International Federation for Information Processing (IFIP) International conference on ICT systems security and privacy protection 2024 (IFIP SEC 2024), 12-14 June 2024, Edinburgh, UK. IFIP Advances in information and communication technology, 710. Cham: Springer [online], pages 451-464. Available from: https://doi.org/10.1007/978-3-031-65175-5_32

The widespread adoption of machine learning (ML) algorithms has revolutionized various aspects of modern life. However, their susceptibility to data poisoning attacks remains a significant concern due to their potential to compromise model integrity... Read More about Cross-validation for detecting label poisoning attacks: a study on random forest algorithm..

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..

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. (2024)
Journal Article
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2024 Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. International journal of information security [online], 23(4), pages 2559-2581. Available from: https://doi.org/10.1007/s10207-024-00855-7

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performa... Read More about Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things..

MADONNA: browser-based malicious domain detection through optimized neural network with feature analysis. (2024)
Presentation / Conference Contribution
SENANAYAKE, J., RAJAPAKSHA, S., YANAI, N., KOMIYA, C. and KALUTARAGE, H. 2024. MADONNA: browser-based malicious domain detection through optimized neural network with feature analysis. In Meyer, N. and Grocholewska-Czuryło, A. (eds.) Revised selected papers from the proceedings of the 38th International conference on ICT systems security and privacy protection (IFIP SEC 2023), 14-16 June 2023, Poznan, Poland. IFIP advances in information and communication technology, 679. Cham: Springer [online], pages 279-292. Available from: https://doi.org/10.1007/978-3-031-56326-3_20

The detection of malicious domains often relies on machine learning (ML), and proposals for browser-based detection of malicious domains with high throughput have been put forward in recent years. However, existing methods suffer from limited accurac... Read More about MADONNA: browser-based malicious domain detection through optimized neural network with feature analysis..

Machine learning algorithm, scaling technique and the accuracy: an application to educational data. (2024)
Presentation / Conference Contribution
WICKRAMASINGHE, I. and KALUTARAGE, H. 2024. Machine learning algorithm, scaling technique and the accuracy: an application to educational data. In Proceedings of the 12th International conference on information and education technology 2024 (ICIET 2024) 18-20 March 2024, Yamaguchi, Japan. Piscataway: IEEE [online], pages 6-12. Available from: https://doi.org/10.1109/iciet60671.2024.10542714

Machine learning (ML) applications in educational data mining have become an increasingly popular research area. Literature indicates a lack of research investigating the impact of data scaling techniques, ML algorithms, and the nature of data on the... Read More about Machine learning algorithm, scaling technique and the accuracy: an application to educational data..

Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops). (2024)
Presentation / Conference Contribution
KATSIKAS, S. et al. (eds.) 2024. Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II. Available from: https://doi.org/10.1007/978-3-031-54129-2

This is the proceedings of seven of the international workshops that were held as part of the 28th edition of the European Symposium on Research in Computer Security (ESORICS).

FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. (2024)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., AL-KADRI, M.O. and PIRAS, L. 2024. FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 426-441. Available from: https://doi.org/10.1007/978-3-031-54129-2_25

Adhering to security best practices during the development of Android applications is of paramount importance due to the high prevalence of apps released without proper security measures. While automated tools can be employed to address vulnerabiliti... Read More about FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI..

Enhancing security assurance in software development: AI-based vulnerable code detection with static analysis. (2024)
Presentation / Conference Contribution
RAJAPAKSHA, S., SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2024. Enhancing security assurance in software development: AI-based vulnerable code detection with static analysis. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 341-356. Available from: https://doi.org/10.1007/978-3-031-54129-2_20

The presence of vulnerable source code in software applications is causing significant reliability and security issues, which can be mitigated by integrating and assuring software security principles during the early stages of the development lifecyc... Read More about Enhancing security assurance in software development: AI-based vulnerable code detection with static analysis..

Mitigating gradient inversion attacks in federated learning with frequency transformation. (2024)
Presentation / Conference Contribution
PALIHAWADANA, C., WIRATUNGA, N., KALUTARAGE, H. and WIJEKOON, A. 2024. Mitigating gradient inversion attacks in federated learning with frequency transformation. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 750-760. Available from: https://doi.org/10.1007/978-3-031-54129-2_44

Centralised machine learning approaches have raised concerns regarding the privacy of client data. To address this issue, privacy-preserving techniques such as Federated Learning (FL) have emerged, where only updated gradients are communicated instea... Read More about Mitigating gradient inversion attacks in federated learning with frequency transformation..

Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. (2024)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., PIRAS, L. and AL-KADRI, M.O. 2024. Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of information security and applications [online], 82, article number 103741. Available from: https://doi.org/10.1016/j.jisa.2024.103741

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to redu... Read More about Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI..

CAN-MIRGU: a comprehensive CAN bus attack dataset from moving vehicles for intrusion detection system evaluation. (2024)
Presentation / Conference Contribution
RAJAPAKSHA, S., MADZUDZO, G., KALUTARAGE, H., PETROVSKI, A. and AL-KADRI, M.O. 2024. CAN-MIRGU: a comprehensive CAN bus attack dataset from moving vehicles for intrusion detection system evaluation. In Proceedings of the 2nd Vehicle security and privacy symposium 2024 (VehicleSec 2024), co-located with the 2024 Network and distributed system security symposium (NDSS 2024), 26 February - 1 March 2024, San Diego, CA, USA. San Diego, CA: NDSS [online], paper 43. Available from: https://doi.org/10.14722/vehiclesec.2024.23043

The Controller Area Network (CAN Bus) has emerged as the de facto standard for in-vehicle communication. However, the CAN bus lacks security features, such as encryption and authentication, making it vulnerable to cyberattacks. In response, the curre... Read More about CAN-MIRGU: a comprehensive CAN bus attack dataset from moving vehicles for intrusion detection system evaluation..

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics. (2023)
Journal Article
SRIVASTAVA, G., MEKALA, M.S., HAJAR, M.S. and KALUTARAGE, H. 2024. C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics. IEEE transactions on consumer electronics [online], 70(1), pages 1556-1565. Available from: https://doi.org/10.1109/TCE.2023.3342635

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is t... Read More about C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics..

3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks. (2023)
Journal Article
HAJAR, M.S., KALUTARAGE, H.K. and AL-KADRI, M.O. 2023. 3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks. Computer networks [online], 237, article number 110073. Available from: https://doi.org/10.1016/j.comnet.2023.110073

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraint... Read More about 3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks..

Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system. (2023)
Journal Article
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and MADZUDZO, G. 2023. Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system. Journal of information security and applications [online], 77, article number 103570. Available from: https://doi.org/10.1016/j.jisa.2023.103570

Modern automobiles are equipped with a large number of electronic control units (ECUs) to provide safe driver assistance and comfortable services. The controller area network (CAN) provides near real-time data transmission between ECUs with adequate... Read More about Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system..