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Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. (2022)
Thesis
ZAKARIYYA, I. 2022. Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987917

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 Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring..

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. (2022)
Thesis
HAJAR, M.S. 2022. A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987863

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 A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks..

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..

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..