PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme
Conference Proceeding
Yaqub, Z., Yigit, Y., Maglaras, L., Tan, Z., & Wooderson, P. (in press)
PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme.
In the rapidly evolving landscape of Intelligent Transportation Systems (ITS), Vehicular Ad-hoc Networks (VANETs) play a critical role in enhancing road safety and traffic flo...
A Probability Mapping-Based Privacy Preservation Method for Social Networks
Conference Proceeding
Li, Q., Wang, Y., Wang, F., Tan, Z., & Wang, C. (2024)
A Probability Mapping-Based Privacy Preservation Method for Social Networks. . https://doi.org/10.1007/978-981-97-1274-8_19
The mining and analysis of social networks can bring significant economic and social benefits. However, it also poses a risk of privacy leakages. Differential privacy is a de ...
How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction
Conference Proceeding
Orme, M., Yu, Y., & Tan, Z. (in press)
How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.
This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and r...
Can Federated Models Be Rectified Through Learning Negative Gradients?
Conference Proceeding
Tahir, A., Tan, Z., & Babaagba, K. O. (2024)
Can Federated Models Be Rectified Through Learning Negative Gradients?. In Big Data Technologies and Applications (18-32). https://doi.org/10.1007/978-3-031-52265-9_2
Federated Learning (FL) is a method to train machine learning (ML) models in a decentralised manner, while preserving the privacy of data from multiple clients. However, FL is...
A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs
Conference Proceeding
McLaren, R. A., Babaagba, K., & Tan, Z. (in press)
A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022
As the field of malware detection continues to grow, a shift in focus is occurring from feature vectors and other common, but easily obfuscated elements to a semantics based a...
Towards Continuous User Authentication Using Personalised Touch-Based Behaviour
Conference Proceeding
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2020)
Towards Continuous User Authentication Using Personalised Touch-Based Behaviour. In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00023
In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for e...
A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network
Conference Proceeding
Thomson, C., Wadhaj, I., Al-Dubai, A., & Tan, Z. (2020)
A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT)https://doi.org/10.1109/WF-IoT48130.2020.9221036
The issue of energy holes, or hotspots, in wireless sensor networks is well referenced. As is the proposed mobilisa-tion of the sink node in order to combat this. However, as ...
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples
Conference Proceeding
Babaagba, K., Tan, Z., & Hart, E. (2020)
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. https://doi.org/10.1109/CEC48606.2020.9185668
Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this,...
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2020)
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. In Applications of Evolutionary Computation. EvoApplications 2020. , (117-132). https://doi.org/10.1007/978-3-030-43722-0_8
In the field of metamorphic malware detection, training a detection model with malware samples that reflect potential mutants of the malware is crucial in developing a model r...
A Multi-attributes-based Trust Model of Internet of Vehicle
Conference Proceeding
Ou, W., Luo, E., Tan, Z., Xiang, L., Yi, Q., & Tian, C. (2019)
A Multi-attributes-based Trust Model of Internet of Vehicle. In Network and System Security. , (706-713). https://doi.org/10.1007/978-3-030-36938-5_45
Internet of Vehicle (IoV) is an open network and it changes in constant, where there are large number of entities. Effective way to keep security of data in IoV is to establis...