Outputs (99)

How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction (2024)
Presentation / Conference Contribution
Orme, M., Yu, Y., & Tan, Z. (2024, May). How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction. Presented at The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy

This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and robots transition from controlled laboratory settings to everyday households... Read More about How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.

A Multi-UAV Cooperative Task Scheduling in Dynamic Environments: Throughput Maximization (2024)
Journal Article
Zhao, L., Li, S., Tan, Z., Hawbani, A., Timotheou, S., & Yu, K. (online). A Multi-UAV Cooperative Task Scheduling in Dynamic Environments: Throughput Maximization. IEEE Transactions on Computers, https://doi.org/10.1109/tc.2024.3483636

Unmanned aerial vehicle (UAV) has been considered a promising technology for advancing terrestrial mobile computing in the dynamic environment. In this research field, throughput, the number of completed tasks and latency are critical evaluation indi... Read More about A Multi-UAV Cooperative Task Scheduling in Dynamic Environments: Throughput Maximization.

Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy (2024)
Presentation / Conference Contribution
Li, Q., Gao, Y., Wang, F., Wang, C., Babaagba, K. O., & Tan, Z. (2024, October). Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy. Presented at 20th EAI International Conference on Security and Privacy in Communication Networks, Dubai, United Arab Emirates

Graph Neural Networks (GNNs) exhibit promise in the domains of network analysis and recommendation systems. Notwithstanding , these networks are susceptible to node injection attacks. To mitigate this vulnerability, the academic community has put for... Read More about Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy.

Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System (2024)
Journal Article
Cheng, H., Tan, Z., Zhang, X., & Liu, Y. (online). Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System. Chinese Journal of Electronics, https://doi.org/10.23919/cje.2023.00.012

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been... Read More about Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System.

Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles (2024)
Journal Article
Zhao, L., Qian, H., Hawbani, A., Al-Dubai, A. Y., Tan, Z., Yu, K., & Zomaya, A. Y. (2024). Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles. IEEE Transactions on Intelligent Transportation Systems, 25(10), 15065-15080. https://doi.org/10.1109/TITS.2024.3398602

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fost... Read More about Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles.

MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer (2024)
Journal Article
Wang, F., Shi, X., Yang, F., Song, R., Li, Q., Tan, Z., & Wang, C. (2024). MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer. Journal of Information Security and Applications, 83, Article 103784. https://doi.org/10.1016/j.jisa.2024.103784

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defend... Read More about MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer.

Detection of Ransomware (2024)
Patent
Buchanan, B., McLaren, P., Russell, G., & Tan, Z. (2024). Detection of Ransomware. US20240152616A1

The present invention relates to a computer program product, a computing device and a method of detecting a file encrypted by ransomware by identifying a file write operation for a file on the computing device and determining if a predetermined numbe... Read More about Detection of Ransomware.

A Probability Mapping-Based Privacy Preservation Method for Social Networks (2024)
Presentation / Conference Contribution
Li, Q., Wang, Y., Wang, F., Tan, Z., & Wang, C. (2023, November). A Probability Mapping-Based Privacy Preservation Method for Social Networks. Presented at The 3rd International Conference on Ubiquitous Security 2023 (UbiSec-2023), Exeter

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 facto standard to prevent such leaks, but it suffers from the high sensitiv... Read More about A Probability Mapping-Based Privacy Preservation Method for Social Networks.

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation (2024)
Journal Article
Fang, M., Yu, L., Xie, H., Tan, Q., Tan, Z., Hussain, A., Wang, Z., Li, J., & Tian, Z. (2024). STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation. IEEE Transactions on Computational Social Systems, 11(4), 5354 - 5366. https://doi.org/10.1109/tcss.2024.3356549

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional ref... Read More about STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation.

Can Federated Models Be Rectified Through Learning Negative Gradients? (2024)
Presentation / Conference Contribution
Tahir, A., Tan, Z., & Babaagba, K. O. Can Federated Models Be Rectified Through Learning Negative Gradients?. Presented at 13th EAI International Conference, BDTA 2023, Edinburgh

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 vulnerable to malicious attacks, such as poisoning attacks, and is challen... Read More about Can Federated Models Be Rectified Through Learning Negative Gradients?.

Self-attention is What You Need to Fool a Speaker Recognition System (2023)
Presentation / Conference Contribution
Wang, F., Song, R., Tan, Z., Li, Q., Wang, C., & Yang, Y. (2023, November). Self-attention is What You Need to Fool a Speaker Recognition System. Presented at The 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023), Exeter, UK

Speaker Recognition Systems (SRSs) are becoming increasingly popular in various aspects of life due to advances in technology. However, these systems are vulnerable to cyber threats, particularly adversarial attacks. Traditional adversarial attack me... Read More about Self-attention is What You Need to Fool a Speaker Recognition System.

TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication (2023)
Presentation / Conference Contribution
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2023, November). TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication. Presented at The 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023), Exeter, UK

We are increasingly required to prove our identity when using smartphones through explicit authentication processes such as passwords or physiological biometrics, e.g., authorising online banking transactions or unlocking smartphones. However, these... Read More about TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices (2023)
Presentation / Conference Contribution
Spalding, A., Tan, Z., & Babaagba, K. O. (2023, November). Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices. Presented at The International Symposium on Intelligent and Trustworthy Computing, Communications, and Networking (ITCCN-2023), Exeter, UK

Data recovery for forensic analysis of both hard drives and solid state media presents its own unique set of challenges. Hard drives face mechanical failures and data fragmentation , but their sequential storage and higher success rates make recovery... Read More about Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices.

Machine Un-learning: An Overview of Techniques, Applications, and Future Directions (2023)
Journal Article
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2024). Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cognitive Computation, 16, 482-506. https://doi.org/10.1007/s12559-023-10219-3

ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting... Read More about Machine Un-learning: An Overview of Techniques, Applications, and Future Directions.

A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing (2023)
Journal Article
Zhao, L., Zhao, Z., Zhang, E., Hawbani, A., Al-Dubai, A., Tan, Z., & Hussain, A. (2023). A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing. IEEE Journal on Selected Areas in Communications, 41(11), 3386-3400. https://doi.org/10.1109/jsac.2023.3310062

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabili... Read More about A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing.

MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification (2023)
Journal Article
Lu, L., Cui, X., Tan, Z., & Wu, Y. (2024). MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(4), 725-736. https://doi.org/10.1109/TCBB.2023.3284846

In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image c... Read More about MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification.

An omnidirectional approach to touch-based continuous authentication (2023)
Journal Article
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2023). An omnidirectional approach to touch-based continuous authentication. Computers and Security, 128, Article 103146. https://doi.org/10.1016/j.cose.2023.103146

This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. While efforts are made to advance touch-based behavioural authentication, researchers often... Read More about An omnidirectional approach to touch-based continuous authentication.

Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation (2023)
Journal Article
Wang, F., Xie, M., Tan, Z., Li, Q., & Wang, C. (2024). Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation. IEEE Transactions on Emerging Topics in Computing, 12(1), 307 - 315. https://doi.org/10.1109/TETC.2023.3244174

In the era of big data, deep learning techniques provide intelligent solutions for various problems in real-life scenarios. However, deep neural networks depend on large-scale datasets including sensitive data, which causes the potential risk of priv... Read More about Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation.

Ensemble learning-based IDS for sensors telemetry data in IoT networks (2022)
Journal Article
Naz, N., Khan, M. A., Alsuhibany, S. A., Diyan, M., Tan, Z., Khan, M. A., & Ahmad, J. (2022). Ensemble learning-based IDS for sensors telemetry data in IoT networks. Mathematical Biosciences and Engineering, 19(10), 10550-10580. https://doi.org/10.3934/mbe.2022493

The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with ot... Read More about Ensemble learning-based IDS for sensors telemetry data in IoT networks.

A novel flow-vector generation approach for malicious traffic detection (2022)
Journal Article
Hou, J., Liu, F., Lu, H., Tan, Z., Zhuang, X., & Tian, Z. (2022). A novel flow-vector generation approach for malicious traffic detection. Journal of Parallel and Distributed Computing, 169, 72-86. https://doi.org/10.1016/j.jpdc.2022.06.004

Malicious traffic detection is one of the most important parts of cyber security. The approaches of using the flow as the detection object are recognized as effective. Benefiting from the development of deep learning techniques, raw traffic can be di... Read More about A novel flow-vector generation approach for malicious traffic detection.