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Dr Harsha Kalutarage


Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. (2022)
Conference Proceeding
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., MADZUDZO, G. and PETROVSKI, A.V. 2022. Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. In Jančárková, T., Visky, G. and Winther, I. (eds.). Proceedings of 14th International conference on Cyber conflict 2022 (CyCon 2022): keep moving, 31 May - 3 June 2022, Tallinn, Estonia. Tallinn: CCDCOE, pages 309-330. Hosted on IEEE Xplore [online]. Available from: https://doi.org/10.23919/CyCon55549.2022.9811048

The growth of information technologies has driven the development of the transportation sector, including connected and autonomous vehicles. Due to its communication capabilities, the controller area network (CAN) is the most widely used in-vehicle c... Read More about Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security..

Developing secured android applications by mitigating code vulnerabilities with machine learning. (2022)
Conference Proceeding
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..

Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks. (2022)
Conference Proceeding
ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2022. Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks. In CPPS '22: proceedings of the 8th ACM (Association for Computing Machinery) Cyber-physical system security workshop 2022 (CPSS '22), co-located with the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIACCS '22) Nagasaki, Japan (virtual event). New York: ACM [online], pages 41-51. Available from: https://doi.org/10.1145/3494107.3522772

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection... Read More about Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks..

Improving intrusion detection through training data augmentation. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Improving intrusion detection through training data augmentation. In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 17. Available from: https://doi.org/10.1109/SIN54109.2021.9699293

Imbalanced classes in datasets are common problems often found in security data. Therefore, several strategies like class resampling and cost-sensitive training have been proposed to address it. In this paper, we propose a data augmentation strategy... Read More about Improving intrusion detection through training data augmentation..

Reasoning with counterfactual explanations for code vulnerability detection and correction. (2021)
Conference Proceeding
WIJEKOON, A. and WIRATUNGA, N. 2021. Reasoning with counterfactual explanations for code vulnerability detection and correction. In Sani, S. and Kalutarage, H. (eds.) AI and cybersecurity 2021 (AI-Cybersec 2021): proceedings of the workshop on AI and cybersecurity (AI-Cybersec 2021) co-located with 41st (British Computer Society's Specialist Group on Artificial Intelligence) SGAI international conference on artificial intelligence (SGAI 2021), 14 December 2021, Cambridge, UK: [virtual conference]. Aachen: CEUR Workshop Proceedings [online], 3125, pages 1-13. Available from: http://ceur-ws.org/Vol-3125/paper1.pdf 14 December 2021, Cambridge, UK: [virtual event]. Aachen: CEUR Workshop Proceedings [online], 3125, pages 1-13. Available from: http://ceur-ws.org/Vol-3125/paper1.pdf

Counterfactual explanations highlight "actionable knowledge" which helps the end-users to understand how a machine learning outcome could be changed to a more desirable outcome. In code vulnerability detection, understanding these "actionable" correc... Read More about Reasoning with counterfactual explanations for code vulnerability detection and correction..

Memory efficient federated deep learning for intrusion detection in IoT networks. (2021)
Conference Proceeding
ZAKARIYYA, A. KALUTARAGE, H. and AL-KADRI, M.O. 2021. Memory efficient federated deep learning for intrusion detection in IoT networks. In Sani, S. and Kalutarage, H. (eds.) AI and cybersecurity 2021 (AI-Cybersec 2021): proceedings of the Workshop on AI and Cybersecurity (AI-Cybersec 2021) co-located with 41st (British Computer Society's Specialist Group on Artificial Intelligence) SGAI international conference on artificial intelligence (SGAI 2021): [virtual conference]. Aachen: CEUR Workshop Proceedings [online], 3125, pages 85-99. Available from: http://ceur-ws.org/Vol-3125/paper7.pdf

Deep Neural Networks (DNNs) methods are widely proposed for cyber security monitoring. However, training DNNs requires a lot of computational resources. This restricts direct deployment of DNNs to resource-constrained environments like the Internet o... Read More about Memory efficient federated deep learning for intrusion detection in IoT networks..

FedSim: similarity guided model aggregation for federated learning. (2021)
Journal Article
PALIHAWADANA, C., WIRATUNGA, N., WIJEKOON, A. and KALUTARAGE, H. 2022. FedSim: similarity guided model aggregation for federated learning. Neurocomputing [online], 483: distributed machine learning, optimization and applications, pages 432-445. Available from: https://doi.org/10.1016/j.neucom.2021.08.141

Federated Learning (FL) is a distributed machine learning approach in which clients contribute to learning a global model in a privacy preserved manner. Effective aggregation of client models is essential to create a generalised global model. To what... Read More about FedSim: similarity guided model aggregation for federated learning..

TrustMod: a trust management module for NS-3 simulator. (2021)
Conference Proceeding
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2021. TrustMod: a trust management module for NS-3 simulator. In Zhao, L., Kumar, N., Hsu, R.C. and Zou, D. (eds.) Proceedings of 20th IEEE (Institute of Electrical and Electronics Engineers) International conference on Trust, security and privacy in computing and communications 2021 (IEEE TrustCom 2021), 20-21 October 2021, Shenyang, China: [virtual event]. Piscataway: IEEE [online], pages 51-60. Available from: https://doi.org/10.1109/TrustCom53373.2021.00025

Trust management offers a further level of defense against internal attacks in ad hoc networks. Deploying an effective trust management scheme can reinforce the overall network security. Regardless of limitations, however, security researchers often... Read More about TrustMod: a trust management module for NS-3 simulator..

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..

Effective detection of cyber attack in a cyber-physical power grid system. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Effective detection of cyber attack in a cyber-physical power grid system. In Arai, K. (ed) Advances in information and communication: proceedings of Future of information and communication conference (FICC 2021), 29-30 April 2021, Vancouver, Canada. Advances in intelligent systems and computing, 1363. Cham: Springer [online], 1, pages 812-829. Available from: https://doi.org/10.1007/978-3-030-73100-7_57

Advancement in technology and the adoption of smart devices in the operation of power grid systems have made it imperative to ensure adequate protection for the cyber-physical power grid system against cyber-attacks. This is because, contemporary cyb... Read More about Effective detection of cyber attack in a cyber-physical power grid system..