Outputs (17)

Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode (2024)
Presentation / Conference Contribution
Buchanan, W., Grierson, S., & Uribe, D. (2024, February). Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode. Presented at 10th International Conference on Information Systems Security and Privacy, Rome, Italy

Biometric data is often highly sensitive, and a leak of this data can lead to serious privacy breaches. Some of the most sensitive of this type of data relates to the usage of DNA data on individuals. A leak of this type of data without consent could... Read More about Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode.

DID:RING: Ring Signatures Using Decentralised Identifiers For Privacy-Aware Identity Proof (2024)
Presentation / Conference Contribution
Kasimatis, D., Grierson, S., Buchanan, W. J., Eckl, C., Papadopoulos, P., Pitropakis, N., Chrysoulas, C., Thomson, C., & Ghaleb, B. (2024, September). DID:RING: Ring Signatures Using Decentralised Identifiers For Privacy-Aware Identity Proof. Paper presented at 2024 IEEE International Conference on Cyber Security and Resilience (CSR), London, UK

Decentralised identifiers have become a standardised element of digital identity architecture, with supra-national organisations such as the European Union adopting them as a key component for a unified European digital identity ledger. This paper de... Read More about DID:RING: Ring Signatures Using Decentralised Identifiers For Privacy-Aware Identity Proof.

VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Ali, M., Dubai, A. A., Pitropakis, N., & Buchanan, W. J. (2024, July). VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography. Presented at 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP), Sousse, Tunisia

In this digital era, ensuring the security of data transmission is critically important. Digital data, especially image data, needs to be secured against unauthorized access. In this regards, this paper presents a robust image encryption scheme named... Read More about VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography.

An Intelligent Digital Twin Model for Attack Detection in Zero-Touch 6G Networks (2024)
Presentation / Conference Contribution
Bolat-Akça, B., Bozkaya-Aras, E., Canberk, B., Buchanan, B., & Schmid, S. (2024, June). An Intelligent Digital Twin Model for Attack Detection in Zero-Touch 6G Networks. Presented at 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA

The rapid adoption of Internet of Things (IoT) services and the increasingly stringent dependability and performance requirements are transforming next-generation wireless network management towards zero-touch 6G networks. Zero-touch management is al... Read More about An Intelligent Digital Twin Model for Attack Detection in Zero-Touch 6G Networks.

Transforming EU Governance: The Digital Integration Through EBSI and GLASS (2024)
Presentation / Conference Contribution
Kasimatis, D., Buchanan, W. J., Abubakar, M., Lo, O., Chrysoulas, C., Pitropakis, N., Papadopoulos, P., Sayeed, S., & Sel, M. (2024, June). Transforming EU Governance: The Digital Integration Through EBSI and GLASS. Presented at 39th IFIP International Conference, Edinburgh, UK

Traditionally, government systems managed citizen identities through disconnected data systems, using simple identifiers and paper-based processes, limiting digital trust and requiring citizens to request identity verification documents. The digital... Read More about Transforming EU Governance: The Digital Integration Through EBSI and GLASS.

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.

ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT (2024)
Presentation / Conference Contribution
Huma, Z. E., Ahmad, J., Hamadi, H. A., Ghaleb, B., Buchanan, W. J., & Jan, S. U. (2024, February). ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT. Presented at 2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates

The Internet of Things (IoT) has become an integral part of modern societies, with devices, networks, and applications offering industrial, economic, and social benefits. However, these devices and networks generate vast amounts of data, making them... Read More about ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT.

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. (online). Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles. IEEE Transactions on Intelligent Transportation Systems, 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.

Exploring DTrace as an Incident Response Tool for Unix Systems (2024)
Presentation / Conference Contribution
Duin, J., Mckeown, S., & Abubakar, M. (2024, June). Exploring DTrace as an Incident Response Tool for Unix Systems. Presented at Cyber Science 2024, Edinburgh, Scotland

Critical National Infrastructure (CNI) is often the target of sophisticated and sustained cyber attacks perpetrated by advanced threat actors with considerable resources. These attacks can lead to interruptions in core services such as energy and wa... Read More about Exploring DTrace as an Incident Response Tool for Unix Systems.

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks (2024)
Journal Article
Bhatti, D. S., Saleem, S., Ali, Z., Park, T., Suh, B., Kamran, A., Buchanan, W. J., & Kim, K. (2024). Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks. IEEE Access, 12, 41499-41516. https://doi.org/10.1109/access.2024.3377144

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head select... Read More about Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks.

PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework (2024)
Presentation / Conference Contribution
Mckeown, S., Aaby, P., & Steyven, A. PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework. Presented at DFRWS EU 2024, Zaragoza, Spain

The automated comparison of visual content is a contemporary solution to scale the detection of illegal media and extremist material, both for detection on individual devices and in the cloud. However, the problem is difficult, and perceptual similar... Read More about PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework.

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.

SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT (2024)
Journal Article
Alshehri, M. S., Ahmad, J., Almakdi, S., Qathrady, M. A., Ghadi, Y. Y., & Buchanan, W. J. (2024). SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT. IEEE Access, 12, https://doi.org/10.1109/access.2024.3371992

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweigh... Read More about SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT.

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?.