Skip to main content

Research Repository

Advanced Search

All Outputs (40)

A nonlinear correlation measure for Intrusion Detection (2012)
Presentation / Conference Contribution
Ambusaidi, M., Lu, L. F., He, X., Tan, Z., Jamdagni, A., & Nanda, P. (2012, November). A nonlinear correlation measure for Intrusion Detection. Paper presented at The 7th International Conference on Frontier of Computer Science and Technology (FCST-12)

The popularity of using internet contains some risks of network attacks. It has attracted the attention of many researchers to overcome this problem. One of the effective ways that plays an important role to achieve higher security and protect networ... Read More about A nonlinear correlation measure for Intrusion Detection.

Triangle-Area-Based Multivariate Correlation Analysis for Effective Denial-of-Service Attack Detection (2012)
Presentation / Conference Contribution
Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2012). Triangle-Area-Based Multivariate Correlation Analysis for Effective Denial-of-Service Attack Detection. . https://doi.org/10.1109/trustcom.2012.284

Cloud computing plays an important role in current converged networks. It brings convenience of accessing services and information to users regardless of location and time. However, there are some critical security issues residing in cloud computing,... Read More about Triangle-Area-Based Multivariate Correlation Analysis for Effective Denial-of-Service Attack Detection.

Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method
Presentation / Conference Contribution
Ou, W., Deng, M., Luo, E., Shi, W., Tan, Z., & Bhuiyan, M. (2019, July). Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method. Presented at The 2019 IEEE International Conference on Cyber Physical and Social Computing (CPSCom-2019), Atlanta, USA

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.

Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy
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.

Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node
Presentation / Conference Contribution
Thomson, C., Wadhaj, I., Tan, Z., & Al-Dubai, A. (2019, August). Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node. Presented at 2019 IEEE International Conference on Smart Internet of Things, Tianjin, China

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.

A Multi-attributes-based Trust Model of Internet of Vehicle
Presentation / Conference Contribution
Ou, W., Luo, E., Tan, Z., Xiang, L., Yi, Q., & Tian, C. (2019, December). A Multi-attributes-based Trust Model of Internet of Vehicle. Presented at 13th International Conference on Network and System Security, Sapporo, Japan

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.

Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2020, April). Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. Presented at EvoStar 2020, Seville, Spain

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 New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network
Presentation / Conference Contribution
Thomson, C., Wadhaj, I., Al-Dubai, A., & Tan, Z. (2020, April). A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network. Presented at IEEE 6th World Forum on Internet of Things, New Orleans, Louisiana, USA

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
Presentation / Conference Contribution
Babaagba, K., Tan, Z., & Hart, E. (2020, July). Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. Presented at The 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK

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.

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour
Presentation / Conference Contribution
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2020, August). Towards Continuous User Authentication Using Personalised Touch-Based Behaviour. Presented at CyberSciTech 2020, Calgary, Canada

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.

TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication
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.

A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling
Presentation / Conference Contribution
Turnbull, L., Tan, Z., & Babaagba, K. (2022, June). A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling. Presented at The 2022 5th IEEE Conference on Dependable and Secure Computing (IEEE DSC 2022), Edinburgh [Online]

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.

A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs
Presentation / Conference Contribution
McLaren, R. A., Babaagba, K., & Tan, Z. (2022, September). A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. Presented at The 8th International Conference on machine Learning, Optimization and Data science - LOD 2022, Certosa di Pontignano, Siena – Tuscany, Italy

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.

Challenges and Considerations in Data Recovery from Solid State Media: A Comparative Analysis with Traditional Devices
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.

Self-attention is What You Need to Fool a Speaker Recognition System
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.

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

Can Federated Models Be Rectified Through Learning Negative Gradients?
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?.

PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme
Presentation / Conference Contribution
Yaqub, Z., Yigit, Y., Maglaras, L., Tan, Z., & Wooderson, P. (2024, April). PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme. Presented at The 20th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2024), Abu Dhabi, UAE

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.

How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction
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.