All Outputs (7)

Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. (2024)
Thesis
OTOKWALA, U.J. 2024. Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2571244

Critical Infrastructures (CI) are essential for various aspects of human activities, spanning across different sectors. However, the integration of Internet of Things (IoT) devices into CI has introduced a new dimension to security challenges due to... Read More about Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures..

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..

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..

Reasoning with counterfactual explanations for code vulnerability detection and correction.
Presentation / Conference Contribution
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: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online], 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.
Presentation / Conference Contribution
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: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online], 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..

AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021)
Presentation / Conference Contribution
SANI, S. and KALUTARAGE, H. (eds.) 2021. AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online]. Available from: https://ceur-ws.org/Vol-3125/

This volume consists of the papers that were presented at the 1st International Workshop on Artificial Intelligence and Cyber Security, co-located with the 41st SGAI International Conference on Artificial Intelligence (AI-2021) on December 14th, 2021... Read More about AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021).

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)
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).