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All Outputs (36)

PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme (2024)
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 flow. However, VANETs face significant security and privacy challenges due to... Read More about PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme.

A Probability Mapping-Based Privacy Preservation Method for Social Networks (2024)
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 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.

How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction (2024)
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 robots transition from controlled laboratory settings to everyday households... Read More about How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.

Can Federated Models Be Rectified Through Learning Negative Gradients? (2024)
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 vulnerable to malicious attacks, such as poisoning attacks, and is challen... Read More about Can Federated Models Be Rectified Through Learning Negative Gradients?.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices (2023)
Conference Proceeding
Spalding, A., Tan, Z., & Babaagba, K. O. (in press). Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices. In Proceedings of the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023)

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.

TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication (2023)
Conference Proceeding
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (in press). TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication.

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.

Self-attention is What You Need to Fool a Speaker Recognition System (2023)
Conference Proceeding
Wang, F., Song, R., Tan, Z., Li, Q., Wang, C., & Yang, Y. (in press). Self-attention is What You Need to Fool a Speaker Recognition System.

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.

A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs (2023)
Conference Proceeding
McLaren, R. A., Babaagba, K., & Tan, Z. (2023). A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II (32-46). https://doi.org/10.1007/978-3-031-25891-6_4

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 approach. This is due to the emergence of more complex malware families that... Read More about A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs.

A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling (2022)
Conference Proceeding
Turnbull, L., Tan, Z., & Babaagba, K. (2022). A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling. In 2022 IEEE Conference on Dependable and Secure Computing (DSC). https://doi.org/10.1109/DSC54232.2022.9888906

Malicious software trends show a persistent yearly increase in volume and cost impact. More than 350,000 new malicious or unwanted programs that target various technologies were registered daily over the past year. Metamorphic malware is a specifical... Read More about A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling.

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour (2020)
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 each user, as behaviour is different amongst humans. Thus, a fixed feature s... Read More about Towards Continuous User Authentication Using Personalised Touch-Based Behaviour.

A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network (2020)
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 the sink node shall still pass some nodes more closely and frequently than... Read More about A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network.

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples (2020)
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, we explore whether machine-learning models can be improved by augmenting t... Read More about Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples.

Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites (2020)
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 resistant to future attacks. In this paper, we use a Multi-dimensional Archi... Read More about Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites.

A Multi-attributes-based Trust Model of Internet of Vehicle (2019)
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 establish a trustworthy mechanism. Through transmission and dissemination of trust,... Read More about A Multi-attributes-based Trust Model of Internet of Vehicle.

Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node (2019)
Conference Proceeding
Thomson, C., Wadhaj, I., Tan, Z., & Al-Dubai, A. (2019). Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node. In 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). https://doi.org/10.1109/SmartIoT.2019.00037

In Wireless Sensor Networks (WSNs) the use of Mobile Sink Nodes (MSNs) has been proposed in order to negate the ”hotspot” issue. This where nodes closest to the sink node shall run out of energy fastest, affecting network lifetime. However, in using... Read More about Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node.

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme (2019)
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2019). Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. In Dependability in Sensor, Cloud, and Big Data Systems and Applications (369-382). https://doi.org/10.1007/978-981-15-1304-6_29

The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware is particularly hard to detect via signature-based intrusion detection syste... Read More about Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme.

Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method (2019)
Conference Proceeding
Ou, W., Deng, M., Luo, E., Shi, W., Tan, Z., & Bhuiyan, M. (2019). Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method. In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (687-693). https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00131

Proof of Work (PoW) is used to provide a consensus mechanism for Bitcoin. In this mechanism, the process of generating a new block in the blockchain is referred to as mining. Such process is intentionally designed to be resource-intensive and time co... Read More about Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method.

A Learning-based Vehicle-Trajectory Generation Method for Vehicular Networking (2019)
Conference Proceeding
Zhao, L., Liu, Y., Al-Dubai, A., Tan, Z., Min, G., & Xu, L. (2019). A Learning-based Vehicle-Trajectory Generation Method for Vehicular Networking. . https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00082

With the rapid development of mobile applications, networking technologies have been constantly evolved to offer a more convenient way of sharing information and online-communication anytime and anywhere. Vehicular networks have the potential to beco... Read More about A Learning-based Vehicle-Trajectory Generation Method for Vehicular Networking.

A 3D Smooth Random Walk Mobility Model for FANETs (2019)
Conference Proceeding
Lin, N., Gao, F., Zhao, L., Al-Dubai, A., & Tan, Z. (2019). A 3D Smooth Random Walk Mobility Model for FANETs. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00075

The number of Unmanned Aerial Vehicles (UAVs) applications has increased over the past few years. Among all scenarios, UAV group consisting multi-UAVs is normally used to provide extensible communications. As a networking solution, Flying Ad Hoc Netw... Read More about A 3D Smooth Random Walk Mobility Model for FANETs.