Publications

Peer-Reviewed Conference and Journal Articles

1. Senanayake, J., Kalutarage, H., Petrovski, A., Piras, L., & 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, 82, 103741.

2. Rajapaksha, S., Madzudzo, G., Kalutarage, H., Petrovski, A., & Al-Kadri, M. O. (2024). CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving Vehicles for Intrusion Detection System Evaluation. In VehicleSec'24 in the Network and Distributed System Security (NDSS) Symposium 2024.

3. Hajar, M. S., Kalutarage, H. K., & Al-Kadri, M. O. (2023). 3R: A reliable multi-agent reinforcement learning-based routing protocol for wireless medical sensor networks. Computer Networks, 237, 110073.

4. Rajapaksha, S., Kalutarage, H., Al-Kadri, M. O., Petrovski, A., & Madzudzo, G. (2023). Beyond vanilla: Improved autoencoder-based ensemble in-vehicle intrusion detection system. Journal of information security and applications, 77, 103570.

5. Zakariyya, I., Kalutarage, H., & Al-Kadri, M. O. (2023). Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. Computers & Security, 133, 103388.

6. Senanayake, J., Kalutarage, H., Al-Kadri, M. O., Petrovski, A., & Piras, L. (2023). Android Code Vulnerabilities Early Detection Using AI-Powered ACVED Plugin. In 36th IFIP Annual Conference on Data and Applications Security and Privacy (pp. 339-357). Cham: Springer Nature Switzerland.

7. Senanayake,J., Rajapaksha, S., Yanai, N., Komiya, C., & Kalutarage, H. (2023). MADONNA: Browser-Based MAlicious Domain Detection through Optimized Neural Network with Feature Analysis. In ICT Systems Security and Privacy Protection: 38th IFIP TC 11 International Conference.

8. Senanayake, J., Kalutarage, H., Petrovski, A., Al-Kadri, M. O., & Piras, L. (2023). FedREVAN: Real-time Detection of Vulnerable Android Source Code Through Federated Neural Network with XAI. In European Symposium on Research in Computer Security (pp. 426-441). Cham: Springer Nature Switzerland.

9. Srivastava, G., Mekala, M. S., Hajar, M. S., & Kalutarage, H. (2023). C-NEST: Cloudlet Based Privacy Preserving Multidimensional Data Stream Approach for Healthcare Electronics. IEEE Transactions on Consumer Electronics.

10. Palihawadana, C., Wiratunga, N., Kalutarage, H., & Wijekoon, A. (2023). Mitigating Gradient Inversion Attacks in Federated Learning with Frequency Transformation. In European Symposium on Research in Computer Security (pp. 750-760). Cham: Springer Nature Switzerland.

11. Rajapaksha, S., Senanayake, J., Kalutarage, H., & Al-Kadri, M. O. (2023). Enhancing Security Assurance in Software Development: AI-Based Vulnerable Code Detection with Static Analysis. In European Symposium on Research in Computer Security (pp. 341-356). Cham: Springer Nature Switzerland.

12. Senanayake, J., Kalutarage, H., Al-Kadri, M. O., Piras, L., & Petrovski, A. (2023). Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. SECRYPT 2023.

13. Rajapaksha, S., Kalutarage, H., Al-Kadri, M. O., Petrovski, A., & Madzudzo, G. (2023). Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning. In VehicleSec 2023 in the Network and Distributed System Security (NDSS) Symposium 2023.

14. Rajapaksha, S., Kalutarage, H., Al-Kadri, M. O., Petrovski, A., Madzudzo, G., & Cheah, M. (2023). Ai-based intrusion detection systems for in-vehicle networks: A survey. ACM Computing Surveys, 55(11), 1-40.

15. Hajar, M. S., Kalutarage, H., & Al-Kadri, M. O. (2023). RRP: A reliable reinforcement learning-based routing protocol for wireless medical sensor networks. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC) (pp. 781-789). IEEE.

16. Hajar, M. S., Kalutarage, H., & Al-Kadri, M. O. (2022). Dqr: A double q learning multi-agent routing protocol for wireless medical sensor network. In International Conference on Security and Privacy in Communication Systems (pp. 611-629). Cham: Springer Nature Switzerland.

17. Senanayake, J., Kalutarage, H., Al-Kadri, M. O., Petrovski, A., & Piras, L. (2023). Android source code vulnerability detection: a systematic literature review. ACM Computing Surveys, 55(9), 1-37.

18. Rajapaksha, S., Senanayake, J., Kalutarage, H., & Al-Kadri, M. O. (2022). AI-Powered Vulnerability Detection for Secure Source Code Development. In International Conference on Information Technology and Communications Security. Springer, Cham.

19. Hajar, M. S., Kalutarage, H., & Al-Kadri, M. O. (2022). A Robust Exploration Strategy in Reinforcement Learning Based on Temporal Difference Error. In AI 2022: Advances in Artificial Intelligence: 35th Australasian Joint Conference, AI 2022, Perth, WA, Australia, December 5–8, 2022, Proceedings (pp. 789-799). Cham: Springer International Publishing.

20. Zakariyya, I., Kalutarage, H., & Al-Kadri, M. O. (2022). Resource Efficient Federated Deep Learning for IoT Security Monitoring. In Attacks and Defenses for the Internet-of-Things: 5th International Workshop, ADIoT 2022 in 27th European Symposium on Research in Computer Security (ESORICS)  Copenhagen, Denmark, September 30, 2022, Revised Selected Papers (pp. 122-142). Cham: Springer Nature Switzerland.

21. Rajapaksha, S., Kalutarage, H., Al-Kadri, M. O., Madzudzo, G., & Petrovski, A. V. (2022). Keep the Moving Vehicle Secure: Context-Aware Intrusion Detection System for In-Vehicle CAN Bus Security. In 2022 14th International Conference on Cyber Conflict: Keep Moving!(CyCon) (Vol. 700, pp. 309-330). IEEE.

22. Senanayake, J., Kalutarage, H., Al-Kadri, M. O., Petrovski, A., & Piras, L. (2022). Developing Secured Android Applications by Mitigating Code Vulnerabilities with Machine Learning. In Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security (pp. 1255-1257).

23. Zakariyya, I., Kalutarage, H., & Al-Kadri, M. O. (2022). Robust, Effective and Resource Efficient Deep Neural Network for Intrusion Detection in IoT Networks. In Proceedings of the 8th ACM on Cyber-Physical System Security Workshop (pp. 41-51).

24. Palihawadana, C., Wiratunga, N., Wijekoon, A., & Kalutarage, H. (2021). FedSim: Similarity Guided Model Aggregation for Federated Learning. Neurocomputing. 

25. Hajar, M. S., Kalutarage, H., & Al-Kadri, M. O. (2021). TrustMod: A Trust Management Module For NS-3 Simulator. In 2020 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE.

26. Hajar, M. S., Al-Kadri, M. O., & Kalutarage, H. K. (2021). A survey on wireless body area networks: architecture, security challenges and research opportunities. Computers & Security, 102211.

27. Wickramasinghe, I., & Kalutarage, H. (2021). Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Computing, 25(3), 2277-2293.

28. Otokwala, U., Petrovski, A., & Kalutarage, H. (2021). Improving Intrusion Detection Through Training Data Augmentation. In 2021 14th International Conference on Security of Information and Networks (SIN) (Vol. 1, pp. 1-8). IEEE.

29. Senanayake, J., Kalutarage, H., & Al-Kadri, M. O. (2021). Android Mobile Malware Detection Using Machine Learning: A Systematic Review. [Special Issue: High Accuracy Detection of Mobile Malware Using Machine Learning]. Electronics, 10(13), 1606.

30. Zakariyya, I., Kalutarage, H., & Al-Kadri, M. O. (2021). Memory Efficient Federated Deep Learning for Intrusion Detection in IoT Networks,  In AI-CyberSec 2021. CEUR Workshop Proceedings.

31. Otokwala, U., Petrovski, A., & Kalutarage, H. (2021). Effective Detection of Cyber Attack in a Cyber-Physical Power Grid System. In Future of Information and Communication Conference (pp. 812-829). Springer, Cham.

32. Hajar, M. S., Al-Kadri, M. O., & Kalutarage, H. (2020). LTMS: A Lightweight Trust Management System for Wireless Medical Sensor Networks. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (pp. 1783-1790). IEEE.

33. Hajar, M. S., Al-Kadri, M. O., & Kalutarage, H. (2020). ETAREE: An Effective Trend-Aware Reputation Evaluation Engine for Wireless Medical Sensor Networks. In 2020 IEEE Conference on Communications and Network Security (CNS) (pp. 1-9). IEEE.

34. Zakariyya, I., Al-Kadri, M. O., & Kalutarage, H. (2020). Resource Efficient Boosting Method for IoT Security Monitoring. In IEEE Consumer Communications and Networking Conference (IEEE CCNC 2021). IEEE.

35. Kalutarage, H. K., Al-Kadri, M. O., Cheah, M., & Madzudzo, G. (2019). Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. In ACM Computer Science in Cars Symposium (pp. 1-8).

36. Jia, G., Miller, P., Hong, X., Kalutarage, H., & Ban, T. (2019). Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder. In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 458-465). IEEE.

37. Zakariyya, I., Al-Kadri, M. O., Kalutarage, H., & Petrovski, A. (2019). Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. SECRYPT 2019.

38. Tomlinson, A., Bryans, J., Shaikh, S. A., & Kalutarage, H. K. (2018). Detection of automotive CAN cyber-attacks by identifying packet timing anomalies in time windows. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) (pp. 231-238). IEEE.

39. Rawal, B. S., Liang, S., Gautam, S., Kalutarage, H. K., & Vijayakumar, P. (2018). Nth order binary encoding with split protocol. International Journal of Rough Sets and Data Analysis (IJRSDA), 5(2), 95-118.

40. Palomares, I., Kalutarage, H., Huang, Y., McCausland, P. M. R., & McWilliams, G. (2017). A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring. In 2017 Joint 17th World Congress of the International Fuzzy Systems Association and the 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) (pp. 1-6). IEEE.

41. Kalutarage, H. K., Nguyen, H. N., & Shaikh, S. A. (2017). Towards a threat assessment framework for apps collusion. Telecommunication Systems, 66(3), 417-430.

42. Rawal, B. S., Kalutarage, H. K., Vivek, S. S., & Pandey, K. (2016). The disintegration protocol: An ultimate technique for cloud data security. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) (pp. 27-34). IEEE.

43. Kalutarage, H., Shaikh, S., Lee, B. S., Lee, C., & Kiat, Y. C. (2015). Early warning systems for cyber defence. In International Workshop on Open Problems in Network Security (pp. 29-42). Springer, Cham.

44. Asavoae, I. M., Blasco, J., Chen, T. M., Kalutarage, H. K., Muttik, I., Nguyen, H. N., ... & Shaikh, S. A. (2016). Towards automated Android app collusion detection. CEUR Workshop Proceedings, 1575,29-37. 

45. Shaikh, S. A., & Kalutarage, H. K. (2016). Effective network security monitoring: from attribution to target-centric monitoring. Telecommunication Systems, 62(1), 167-178.

46. Kalutarage, H. K., Shaikh, S. A., Wickramasinghe, I. P., Zhou, Q., & James, A. E. (2015). Detecting stealthy attacks: Efficient monitoring of suspicious activities on computer networks. Computers & Electrical Engineering, 47, 327-344.

47. Garcia-Perez, A., Shaikh, S. A., Kalutarage, H. K., & Jahantab, M. (2015). Towards a knowledge-based approach for effective decision-making in railway safety. Journal of Knowledge Management, 19(3), 641-659.

48. Kalutarage, H. K., Lee, C., Shaikh, S. A., & Sung, F. L. B. (2015). Towards an early warning system for network attacks using Bayesian inference. In 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing (pp. 399-404). IEEE.

49. Muttik, I., Blasco, J., Chen, T., Kalutarage, H., & Shaikh, S. (2015). Android - Collusion Conspiracy. In Proceedings of the 18th Association of Anti-virus Asia Researchers International Conference. 

50. Kalutarage, H. K., Shaikh, S. A., Zhou, Q., & James, A. E. (2013). Tracing sources of anonymous slow suspicious activities. In International Conference on Network and System Security (pp. 122-134). Springer, Berlin, Heidelberg.

51. Kalutarage, H. K., Shaikh, S. A., Zhou, Q., & James, A. E. (2013). Monitoring for slow suspicious activities using a target-centric approach. In International Conference on Information Systems Security (pp. 163-168). Springer, Berlin, Heidelberg.

52. Kalutarage, H. K., Shaikh, S. A., Zhou, Q., & James, A. E. (2013). How do we effectively monitor for slow suspicious activities? In ESSoS Doctoral Symposium 2013 (p. 36).

53. Kalutarage, H. K., Shaikh, S. A., Zhou, Q., & James, A. E. (2012). Sensing for suspicion at scale: A Bayesian approach for cyber conflict attribution and reasoning. In 2012 4th International Conference on Cyber Conflict (CYCON 2012) (pp. 1-19). IEEE.

54. Kalutarage, H. K., Krishnan, P., & Shaikh, S. A. (2012). A certification process for Android applications. In International Conference on Software Engineering and Formal Methods (pp. 288-303). Springer, Berlin, Heidelberg.

Peer-Reviewed Book Chapters

1. Hajar, M. S., Kalutarage, H. K., & Al-Kadri, M. O. (2023). Security Challenges in Wireless Body Area Networks for Smart Healthcare. In Artificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare (pp. 255-286). CRC Press.

2. Kalutarage, H. K., & Shaikh, S. A. (2019). Feature Trade-Off Analysis for Reconnaissance Detection. In Data Science for Cyber-Security (pp. 95-126).

3. Asăvoae, I. M., Blasco, J., Chen, T. M., Kalutarage, H. K., Muttik, I., Nguyen, H. N., ... & Shaikh, S. A. (2017). Detecting malicious collusion between mobile software applications: the Android TM case. In Data Analytics and Decision Support for Cybersecurity (pp. 55-97). Springer, Cham.

Journal Editing

1. Hajar, M. S., Kalutarage, H., & Tariq, F. (2024). Special issue: Advances in Security Countermeasures for Medical Sensor Networks in the Internet of Things Journal https://www.sciencedirect.com/journal/internet-of-things

2. Kalutarage, H. K., Wiratunga, N. & Sani, S. (Eds.). (2021). Special Issue: Selected Papers from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence, Electronics.

Book Editing

1. Carrascosa, I. P., Kalutarage, H. K., & Huang, Y. (Eds.). (2017). Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. Springer.

Volume Editing

1. Katsikas, Sokratis, et al (Eds.). (2024). Computer Security. ESORICS 2023 International Workshops: CPS4CIP, ADIoT, SecAssure, WASP, TAURIN, PriST-AI, and SECAI.  The Hague, The Netherlands, September 25–29, 2023, Revised Selected Papers, Part II (Vol. 14399). Springer Nature. https://link.springer.com/book/10.1007/978-3-031-54129-2?page=3#toc

2. Proceedings of the Workshop on AI and Cybersecurity (AI-Cybersec 2021) CEUR Workshop Proceedings. https://ceur-ws.org/Vol-3125/

Patents

1. Shaikh, S. A., & Kalutarage, H. K. (2020). U.S. Patent No. 10,681,059. Washington, DC: U.S. Patent and Trademark Office.

Theses

1. Kalutarage, H. K. (2013). Effective monitoring of slow suspicious activites on computer networks (Doctoral dissertation, Coventry University).

2. Kumara, K. H. (2009). Text-to-speech synthesis for Sinhala language (MPhil dissertation, University of Kelaniya). http://repository.kln.ac.lk/handle/123456789/451

Peer-Reviewed Abstracts/Posters in Conferences, Workshops and Symposia

1. LAWANI, A., SINGH, A., MCNEIL, A., DURACK, B. and KALUTARAGE, H. 2019. Strengthening student engagement: evaluating the role of the digital skills agenda in higher education. Presented at the 2019 Department for the Enhancement of Learning, Teaching and Access (DELTA) learning and teaching conference (LTC 2019): learning without borders, 2 May 2019, Aberdeen, UK.

2. Kalutarage, H. K. (2015). Effective monitoring of slow suspicious activities on computer networks. The 5th PhD School on Traffic Monitoring and Analysis, Barcelona, Spain (poster)

3. Kumara, K. H., & Dias, N. G. J. (2009). A tool for automatic derivation of phone transitions for the creation of a diphone database for Sinhala text-to-speech synthesis. Research Symposium 2009-Faculty of Graduate Studies, University of Kelaniya. (abstract)

4. Dias, N. G. J., Kumara, K. H., & Dolawattha, D. D. M. (2009). An analysis of sound parameters for prosodic modelling in Sinhala text-to-speech synthesis. Research Symposium 2009-Faculty of Graduate Studies, University of Kelaniya. (abstract)

5. Kumara, K. H., Dias, N. G. J., & Wickramasinghe, R. (2007). MBROLA Formatted Diphone Database for Sinhala Language. Research Symposium 2007-Faculty of Graduate Studies, University of Kelaniya. (abstract)

Published Whitepapers/Magazine articles

1. Middleton, C., Kalutarage, H., Al-kadri, O., & Ahriz, H. (2021). Holistic Security and Risk Intelligence: Are Current Risk Management Methods Leading to Breach?. techrxiv.org

2. Kalutarage, H., Mitra, B., & McCausland, R. (2018). Modelling IoT Anomaly Detection. ITNOW, 60(3), 44-45.

3. Asavoae, I. M., Blasco, J., Chen, T. M., Kalutarage, H. K., Muttik, I., Nguyen, H. N., ... & Shaikh, S. A. (2018). Distinguishing between malicious app collusion and benign app collaboration: a machine-learning approach. VirusBulletin. https://www.virusbulletin.com/virusbulletin/2018/03/distinguishing-between-malicious-app-collusion-and-benign-app-collaboration-machine-learning-approach/

4. Kalutarage, H. K., Krishnan, P., & Shaikh, S. A. (2013). Android revolution. ITNow, 55(1), 36-37.

5. Stedmon, A., Shaikh, S., Richards, D., Kalutarage, H., Huddlestone,J., & Davison, R. (2014 December). Cyber security and insider threats. The Ergonomist https://www.ergonomics.org.uk/Public/Resources/Articles/Cyber_security_and_insider_threats.aspx