Outputs (63)

Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling (2025)
Journal Article
Huang, Z., Liu, X., & Romdhani, I. (online). Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling. Journal of Data Science and Intelligent Systems, https://doi.org/10.47852/bonviewJDSIS52023947

The study introduces a machine learning model for BMM (Building Maintenance Management), which utilized IPA (Intelligent Process Automation) to predict the material stock required in a period, to manage the cost efficiency. Traditional BMM approaches... Read More about Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling.

Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption (2025)
Journal Article
Buchanan, W. J., & Ali, H. (2025). Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption. Cryptography, 9(2), Article 33. https://doi.org/10.3390/cryptography9020033

The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. To overcome the existing privacy issues, we can apply fully homomorphic encryption (FHE) to encrypt... Read More about Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption.

FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things (2025)
Journal Article
Wang, F., Huo, J., Wang, W., Zhang, X., Liu, Y., Tan, Z., & Wang, C. (online). FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things. IEEE Internet of Things Journal, https://doi.org/10.1109/JIOT.2025.3571432

Smart Internet of Things (IoT) devices generate vast, distributed data, and their limited computational and storage capacities complicate data protection. Federated Learning (FL) enables collaborative model training across clients, enhancing performa... Read More about FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things.

Forensic Joint Photographic Experts Group (JPEG) Watermarking for Disk Image Leak Attribution: An Adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) Approach (2025)
Journal Article
Onyeashie, B. I., Leimich, P., McKeown, S., & Russell, G. (2025). Forensic Joint Photographic Experts Group (JPEG) Watermarking for Disk Image Leak Attribution: An Adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) Approach. Electronics, 14(9), Article 1800. https://doi.org/10.3390/electronics14091800

This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic invest... Read More about Forensic Joint Photographic Experts Group (JPEG) Watermarking for Disk Image Leak Attribution: An Adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) Approach.

Exploring DTrace as an Incident Response Tool for Unix Systems (2025)
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 wat... Read More about Exploring DTrace as an Incident Response Tool for Unix Systems.

LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts (2025)
Journal Article
Kumar, G., Saha, R., Conti, M., & Buchanan, W. J. (online). LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts. IEEE Transactions on Services Computing, https://doi.org/10.1109/tsc.2025.3562323

Smart contracts are integral to decentralized systems like blockchains and enable the automation of processes through programmable conditions. However, their immutability, once deployed, poses challenges when addressing errors or bugs. Existing solut... Read More about LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts.

A Lightweight Image Steganography Scheme Based on Invertible Neural Network Architecture with Progressive Channel Attention (2025)
Presentation / Conference Contribution
Li, Q., Yang, R., Wang, F., Wang, C., Zhang, X., Babaagba, K., & Tan, Z. (2025, July). A Lightweight Image Steganography Scheme Based on Invertible Neural Network Architecture with Progressive Channel Attention. Presented at EAI SecureComm 2025, Xiangtan, China

Post-Quantum Migration of the Tor Application (2025)
Journal Article
Berger, D., Lemoudden, M., & Buchanan, W. J. (2025). Post-Quantum Migration of the Tor Application. Journal of Cybersecurity and Privacy, 5(2), Article 13. https://doi.org/10.3390/jcp5020013

The efficiency of Shor's and Grover's algorithms and the advancement of quantum computers implies that the cryptography used until now to protect one's privacy is potentially vulnerable to retrospective decryption, also known as the harvest now, decr... Read More about Post-Quantum Migration of the Tor Application.

Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques (2025)
Journal Article
Konstantinou, A., Kasimatis, D., Buchanan, W. J., Ullah Jan, S., Ahmad, J., Politis, I., & Pitropakis, N. (2025). Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques. Machine Learning and Knowledge Extraction, 7(2), Article 31. https://doi.org/10.3390/make7020031

This paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend... Read More about Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques.

Beyond Hamming Distance: Exploring Spatial Encoding in Perceptual Hashes (2025)
Presentation / Conference Contribution
McKeown, S. (2025, April). Beyond Hamming Distance: Exploring Spatial Encoding in Perceptual Hashes. Presented at DFRWS EU 2025, Brno, Czech Republic

Forensic analysts are often tasked with analysing large volumes of data in modern investigations, and frequently make use of hashing technologies to identify previously encountered images. Perceptual hashes, which seek to model the semantic (visual)... Read More about Beyond Hamming Distance: Exploring Spatial Encoding in Perceptual Hashes.

Wireless Rechargeable Sensor Networks: Energy Provisioning Technologies, Charging Scheduling Schemes, and Challenges (2025)
Journal Article
Abdel Aziz, S., Wang, X., Hawbani, A., Qureshi, . B., Alsamhi, S. H., Alabsi, A., Zhao, L., Al-Dubai, A., & Ismail, . A. (online). Wireless Rechargeable Sensor Networks: Energy Provisioning Technologies, Charging Scheduling Schemes, and Challenges. IEEE Transactions on Sustainable Computing, https://doi.org/10.1109/TSUSC.2025.3549414

Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among... Read More about Wireless Rechargeable Sensor Networks: Energy Provisioning Technologies, Charging Scheduling Schemes, and Challenges.

Multi-Agent Deep Reinforcement Learning-Based Cooperative Perception and Computation in VEC (2025)
Journal Article
Zhao, L., Li, L., Tan, Z., Hawbani, A., He, Q., & Liu, Z. (online). Multi-Agent Deep Reinforcement Learning-Based Cooperative Perception and Computation in VEC. IEEE Internet of Things Journal, https://doi.org/10.1109/jiot.2025.3546915

Connected and autonomous vehicles (CAVs) are an important paradigm of intelligent transportation systems. Cooperative perception (CP) and vehicular edge computing (VEC) enhance CAVs’ perception capacity of the region of interest (RoI) while alleviati... Read More about Multi-Agent Deep Reinforcement Learning-Based Cooperative Perception and Computation in VEC.

Dynamic Caching Dependency-Aware Task Offloading in Mobile EdgeComputing (2025)
Journal Article
Zhao, L., Zhao, Z., Hawbani, A., Liu, Z., Tan, Z., & Yu, K. (2025). Dynamic Caching Dependency-Aware Task Offloading in Mobile EdgeComputing. IEEE Transactions on Computers, 74(5), 1510-1523. https://doi.org/10.1109/tc.2025.3533091

Mobile Edge Computing (MEC) is a distributed computing paradigm that provides computing capabilities at the periphery of mobile cellular networks. This architecture empowers Mobile Users (MUs) to offload computation-intensive applications to large-sc... Read More about Dynamic Caching Dependency-Aware Task Offloading in Mobile EdgeComputing.

IoT Authentication Protocols: Challenges, and Comparative Analysis (2025)
Journal Article
Alsheavi, A., Hawbani, A., Othman, W., Wang, X., Qaid, G. R. S., Zhao, L., Al-Dubai, A., Liu, Z., Ismail, A., Haveri, R. H., Alsamhi, S. H., & Al-Qaness, M. A. A. (2025). IoT Authentication Protocols: Challenges, and Comparative Analysis. ACM computing surveys, 57(5), Article 116. https://doi.org/10.1145/3703444

In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a sub... Read More about IoT Authentication Protocols: Challenges, and Comparative Analysis.

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.

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.

Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations (2024)
Presentation / Conference Contribution
Almaini, A., Koßmann, T., Folz, J., Schramm, M., Heigl, M., & Al-Dubai, A. (2024, June). Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations. Presented at UNet24: The International Conference on Ubiquitous Networking, Marrakesh, Morocco

Recent advancements in Software-Defined Networking (SDN) have facilitated its deployment across diverse network types, including edge networks. Given the broad applicability of SDN and the complexity of large-scale environments, establishing a compre... Read More about Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations.

An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata (2024)
Journal Article
Rizwan, M., Hawbani, A., Xingfu, W., Anjum, A., Angin, P., Sever, Y., Chen, S., Zhao, L., & Al-Dubai, A. (online). An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata. IEEE Transactions on Big Data, https://doi.org/10.1109/TBDATA.2024.3495497

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vert... Read More about An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata.

CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems (2024)
Journal Article
Zhao, L., Mao, C., Wan, S., Hawbani, A., Al-Dubai, A. Y., Min, G., & Zomaya, A. Y. (2025). CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems. IEEE Transactions on Mobile Computing, 24(3), 2331-2345. https://doi.org/10.1109/tmc.2024.3492148

As a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to o... Read More about CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems.

Contributions to Crypto-Ransomware Analysis and Detection (2024)
Thesis
Davies, S. Contributions to Crypto-Ransomware Analysis and Detection. (Thesis). Edinburgh Napier University. http://researchrepository.napier.ac.uk/Output/3790160

Ransomware poses a severe and evolving threat to cyber security, demanding continuous advancements in analysis and detection techniques. This thesis successfully tackles several critical research gaps in this domain, offering essential resources and... Read More about Contributions to Crypto-Ransomware Analysis and Detection.