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All Outputs (48)

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..

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..

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..

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..

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..

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..

Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. (2023)
Journal Article
ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2023. Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. Computer and security [online], 133, article 103388. Available from: https://doi.org/10.1016/j.cose.2023.103388

The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenge... Read More about Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring..

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..

AI-based intrusion detection systems for in-vehicle networks: a survey. (2023)
Journal Article
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A., MADZUDZO, G. and CHEAH, M. 2023. Al-based intrusion detection systems for in-vehicle networks: a survey. ACM computing survey [online], 55(11), article no. 237, pages 1-40. Available from: https://doi.org/10.1145/3570954

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to nume... Read More about AI-based intrusion detection systems for in-vehicle networks: a survey..

DQR: a double Q learning multi agent routing protocol for wireless medical sensor network. (2023)
Presentation / Conference Contribution
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2023. DQR: a double Q learning multi agent routing protocol for wireless medical sensor network. In Li, F., Liang, K., Lin, Z. and Katsikas, S.K. (eds.) Security and privacy in communication networks: proceedings of the 18th EAI (European Alliance for Innovation) Security and privacy in communication networks 2022 (EAI SecureComm 2022), 17-19 October 2022, Kansas City, USA. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 462. Cham: Springer [online], pages 611-629. Available from: https://doi.org/10.1007/978-3-031-25538-0_32

Wireless Medical Sensor Network (WMSN) offers innovative solutions in the healthcare domain. It alleviates the patients' everyday life difficulties and supports the already overloaded medical staff with continuous monitoring tools. However, widesprea... Read More about DQR: a double Q learning multi agent routing protocol for wireless medical sensor network..

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..

RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks. (2023)
Presentation / Conference Contribution
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2023. RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks. In Proceedings of the 20th IEEE (Institute of Electrical and Electronics Engineers) Consumer communications and networking conference 2023 (CCNC 2023), 8-11 January 2023, Las Vegas, USA. Piscataway: IEEE [online], pages 781-789. Available from: https://doi.org/10.1109/CCNC51644.2023.10060225

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widesp... Read More about RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks..