All Outputs (30)

A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs (2023)
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.

A Generative Neural Network for Enhancing Android Metamorphic Malware Detection based on Behaviour Profiling (2022)
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.

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour (2020)
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.

A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network (2020)
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 (2020)
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.

Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites (2020)
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 Multi-attributes-based Trust Model of Internet of Vehicle (2019)
Presentation / Conference Contribution
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)
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.

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme (2019)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2019, November). Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. Presented at The 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019), Guangzhou, China

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)
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.

A 3D Smooth Random Walk Mobility Model for FANETs (2019)
Presentation / Conference Contribution
Lin, N., Gao, F., Zhao, L., Al-Dubai, A., & Tan, Z. (2019, August). A 3D Smooth Random Walk Mobility Model for FANETs. Presented at The 21 IEEE High Performance Computing and Communications (HPCC), Zhangjiajie, Hunan, China

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.

A Learning-based Vehicle-Trajectory Generation Method for Vehicular Networking (2019)
Presentation / Conference Contribution
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.

Reviving legacy enterprise systems with microservice-based architecture within cloud environments (2019)
Presentation / Conference Contribution
Habibullah, S., Liu, X., Tan, Z., Zhang, Y., & Liu, Q. (2019). Reviving legacy enterprise systems with microservice-based architecture within cloud environments. In Computer Science Conference Proceedings. https://doi.org/10.5121/csit.2019.90713

Evolution has always been a challenge for enterprise computing systems. The microservice based architecture is a new design model which is rapidly becoming one of the most effective means to re-architect legacy enterprise systems and to reengineer th... Read More about Reviving legacy enterprise systems with microservice-based architecture within cloud environments.

A Chain Topology for Efficient Monitoring of Food Grain Storage using Smart Sensors (2018)
Presentation / Conference Contribution
Kumar Mishra, A., Kumar Tripathy, A., Obaidat, M. S., Tan, Z., Prasad, M., Sadoun, B., & Puthal, D. (2018, July). A Chain Topology for Efficient Monitoring of Food Grain Storage using Smart Sensors. Presented at The 15th International Joint Conference on e-Business, Porto, Portugal

Due to lack of an efficient monitoring system to periodically record environmental parameters for food grain storage, a huge loss of food grains in storage is reported every year in many developing countries, especially south-Asian countries. Althoug... Read More about A Chain Topology for Efficient Monitoring of Food Grain Storage using Smart Sensors.

A framework for data security in cloud using collaborative intrusion detection scheme (2017)
Presentation / Conference Contribution
Nagar, U., Nanda, P., He, X., & Tan, Z. (. (2017, October). A framework for data security in cloud using collaborative intrusion detection scheme. Presented at Proceedings of the 10th International Conference on Security of Information and Networks - SIN '17, Jaipur, India

Cloud computing offers an on demand, elastic, global network access to a shared pool of resources that can be configured on user demand. It offers a unique pay-as-you go feature which is based on measured usage and can be compared to other utility se... Read More about A framework for data security in cloud using collaborative intrusion detection scheme.

An Intrusion Detection System Based on Polynomial Feature Correlation Analysis (2017)
Presentation / Conference Contribution
Li, Q., Tan, Z., Jamdagni, A., Nanda, P., He, X., & Han, W. (2017, August). An Intrusion Detection System Based on Polynomial Feature Correlation Analysis. Presented at 2017 IEEE Trustcom/BigDataSE/ICESS

This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic rel... Read More about An Intrusion Detection System Based on Polynomial Feature Correlation Analysis.

An Improvement of Tree-Rule Firewall for a Large Network: Supporting Large Rule Size and Low Delay (2017)
Presentation / Conference Contribution
Chomsiri, T., He, X., Nanda, P., & Tan, Z. (2017). An Improvement of Tree-Rule Firewall for a Large Network: Supporting Large Rule Size and Low Delay. In 2016 IEEE Trustcom/BigDataSE/I​SPA (178-184). https://doi.org/10.1109/trustcom.2016.0061

The firewalls were invented since 1990s [1] and have been developed to operate more secure and faster. From the first era of the firewalls until today, they still regulate packet based on a listed rule. The listed rule is the set of rule sequence whi... Read More about An Improvement of Tree-Rule Firewall for a Large Network: Supporting Large Rule Size and Low Delay.

A Novel Feature Selection Approach for Intrusion Detection Data Classification (2014)
Presentation / Conference Contribution
Ambusaidi, M. A., He, X., Tan, Z., Nanda, P., Lu, L. F., & Nagar, U. T. (2014). A Novel Feature Selection Approach for Intrusion Detection Data Classification. . https://doi.org/10.1109/trustcom.2014.15

Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks ar... Read More about A Novel Feature Selection Approach for Intrusion Detection Data Classification.

A Robust Authentication Scheme for Observing Resources in the Internet of Things Environment (2014)
Presentation / Conference Contribution
Jan, M. A., Nanda, P., He, X., Tan, Z., & Liu, R. P. (2014, September). A Robust Authentication Scheme for Observing Resources in the Internet of Things Environment. Presented at 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications

The Internet of Things is a vision that broadens the scope of the internet by incorporating physical objects to identify themselves to the participating entities. This innovative concept enables a physical device to represent itself in the digital wo... Read More about A Robust Authentication Scheme for Observing Resources in the Internet of Things Environment.

Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning (2014)
Presentation / Conference Contribution
Li, W., Meng, W., Tan, Z., & Xiang, Y. (2014, September). Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning. Presented at 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications

The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training d... Read More about Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning.