All Outputs (11)

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..

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..

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..

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. (2023)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. In De Capitani di Vimercati, S. and Samarati, P. (eds.) Proceedings of the 20th International conference on security and cryptography, 10-12 July 2023, Rome, Italy, volume 1. Setúbal: SciTePress [online], pages 659-666. Available from: https://doi.org/10.5220/0012060400003555

Ensuring the security of Android applications is a vital and intricate aspect requiring careful consideration during development. Unfortunately, many apps are published without sufficient security measures, possibly due to a lack of early vulnerabili... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models..

Android code vulnerabilities early detection using AI-powered ACVED plugin. (2023)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2023. Android code vulnerabilities early detection using AI-powered ACVED plugin. In Atluri, V. and Ferrara, A.L. (eds.) Data and applications security and privacy XXXVII; proceedings of the 37th annual IFIP WG (International Federation for Information Processing Working Group) 11.3 Data and applications security and privacy 2023 (DBSec 2023), 19-21 July 2023, Sophia-Antipolis, France. Lecture notes in computer science (LNCS), 13942. Cham: Springer [online], pages 339-357. Available from: https://doi.org/10.1007/978-3-031-37586-6_20

During Android application development, ensuring adequate security is a crucial and intricate aspect. However, many applications are released without adequate security measures due to the lack of vulnerability identification and code verification at... Read More about Android code vulnerabilities early detection using AI-powered ACVED plugin..

AI-powered vulnerability detection for secure source code development. (2023)
Presentation / Conference Contribution
RAJAPAKSHA, S., SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2023. AI-powered vulnerability detection for secure source code development. In Bella, G., Doinea, M. and Janicke, H. (eds.) Innovative security solutions for information technology and communications: revised selected papers of the 15th International conference on Security for information technology and communications 2022 (SecITC 2022), 8-9 December 2022, [virtual conference]. Lecture notes in computer sciences, 13809. Cham: Springer [online], pages 275-288. Available from: https://doi.org/10.1007/978-3-031-32636-3_16

Vulnerable source code in software applications is causing paramount reliability and security issues. Software security principles should be integrated to reduce these issues at the early stages of the development lifecycle. Artificial Intelligence (... Read More about AI-powered vulnerability detection for secure source code development..

Android source code vulnerability detection: a systematic literature review. (2023)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2023. Android source code vulnerability detection: a systematic literature review. ACM computing surveys [online], 55(9), article 187, pages 1-37. Available from: https://doi.org/10.1145/3556974

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not add... Read More about Android source code vulnerability detection: a systematic literature review..

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset] (2022)
Data
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models [Dataset]. Hosted on GitHub (online). Available from: https://github.com/softwaresec-labs/LVDAndro

Many of the Android apps get published without appropriate security considerations, possibly due to not verifying code or not identifying vulnerabilities at the early stages of development. This can be overcome by using an AI based model trained on a... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset].

Developing secured android applications by mitigating code vulnerabilities with machine learning. (2022)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2022. Developing secured android applications by mitigating code vulnerabilities with machine learning. In ASIA CCS '22: proceedings of the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIA CCS 2022), 30 May - 3 June 2022, Nagasaki, Japan. New York: ACM [online], pages 1255-1257. Available from: https://doi.org/10.1145/3488932.3527290

Mobile application developers sometimes might not be serious about source code security and publish apps to the marketplaces. Therefore, it is essential to have a fully automated security solutions generator to integrate security-by-design into the d... Read More about Developing secured android applications by mitigating code vulnerabilities with machine learning..

Android mobile malware detection using machine learning: a systematic review. (2021)
Journal Article
SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2021. Android mobile malware detection using machine learning: a systematic review. Electronics [online], 10(13), article 1606. Available from: https://doi.org/10.3390/electronics10131606

With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and... Read More about Android mobile malware detection using machine learning: a systematic review..