Outputs (14)

Enhancing the construction of attacker personas in cybersecurity software designs using case law-based facts. [Preprint] (2024)
Working Paper
ILESANMI, O., FAILY, S., NICHO, M. and MCDERMOTT, C. 2024. Enhancing the construction of attacker personas in cybersecurity software designs using case law-based facts. [Preprint]. Hosted on SSRN [online]. Available from: https://doi.org/10.2139/ssrn.4812698

Thwarting potential attackers is always at the heart of cybersecurity software designs. This interdisciplinary paper in computing science and law investigates the possibility of building attacker personas through reliance on case law facts. To combat... Read More about Enhancing the construction of attacker personas in cybersecurity software designs using case law-based facts. [Preprint].

Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning. (2024)
Journal Article
YAN, Y., REN, J., SUN, H. and WILLIAMS, R. 2024. Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning. IEEE transactions on industrial informatics [online], Early Access. Available from: https://doi.org/10.1109/TII.2024.3384609

Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus... Read More about Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning..

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], Latest Articles. 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)
Conference Proceeding
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..

CIA security for internet of vehicles and blockchain-AI integration. (2024)
Journal Article
HAI, T., AKSOY, M., IWENDI, C., IBEKE, E. and MOHAN, S. 2024. CIA security for internet of vehicles and blockchain-AI integration. Journal of grid computing [online], 22(2), article number 43. Available from: https://doi.org/10.1007/s10723-024-09757-3

The lack of data security and the hazardous nature of the Internet of Vehicles (IoV), in the absence of networking settings, have prevented the openness and self-organization of the vehicle networks of IoV cars. The lapses originating in the areas of... Read More about CIA security for internet of vehicles and blockchain-AI integration..

Enhancing security assurance in software development: AI-based vulnerable code detection with static analysis. (2024)
Conference Proceeding
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..

FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. (2024)
Conference Proceeding
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..

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..

Factors influencing mobile app user experience: an analysis of education app user reviews. (2024)
Conference Proceeding
ARAMBEPOLA, N., MUNASINGHE, L. and WARNAJITH, N. 2024. Factors influencing mobile app user experience: an analysis of education app user reviews. In 4th International conference on advanced research in computing 2024 (ICARC 2024), 21-24 February 2024, Belihuloya, Sri Lanka. Piscataway: IEEE [online], pages 223-228. Available from: https://doi.org/10.1109/ICARC61713.2024.10499727

In the competitive digital world, user reviews considered as the most vital source of user feedback, provide valuable insights that reflect the success of software applications in terms of user experience (UX). As user-generated content grows exponen... Read More about Factors influencing mobile app user experience: an analysis of education app user reviews..

Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. (2024)
Journal Article
HAYNES, S.C., JOHNSTON, P. and ELYAN, E. 2024. Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. Multimedia tools and applications [online], Latest Articles. Available from: https://doi.org/10.1007/s11042-024-18543-y

The generalisability of deep neural network classifiers is emerging as one of the most important challenges of our time. The recent COVID-19 pandemic led to a surge of deep learning publications that proposed novel models for the detection of COVID-1... Read More about Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers..