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Outputs (408)

LiSP-XK: Extended Light-Weight Signcryption for IoT in Resource-Constrained Environments (2021)
Journal Article
Kim, T., Kumar, G., Saha, R., Buchanan, W. J., Devgun, T., & Thomas, R. (2021). LiSP-XK: Extended Light-Weight Signcryption for IoT in Resource-Constrained Environments. IEEE Access, 9, 100972-100980. https://doi.org/10.1109/access.2021.3097267

There is an increasing drive to provide improved levels of trust within an Internet-of-Things (IoTs) environments, but the devices and sensors used tend to be limited in their capabilities for dealing with traditional cryptography methods. Resource c... Read More about LiSP-XK: Extended Light-Weight Signcryption for IoT in Resource-Constrained Environments.

Differential Area Analysis for Ransomware Attack Detection within Mixed File Datasets (2021)
Journal Article
Davies, S. R., Macfarlane, R., & Buchanan, W. J. (2021). Differential Area Analysis for Ransomware Attack Detection within Mixed File Datasets. Computers and Security, 108, Article 102377. https://doi.org/10.1016/j.cose.2021.102377

The threat from ransomware continues to grow both in the number of affected victims as well as the cost incurred by the people and organisations impacted in a successful attack. In the majority of cases, once a victim has been attacked there remain o... Read More about Differential Area Analysis for Ransomware Attack Detection within Mixed File Datasets.

Newly Engineered Energy-based Features for Supervised Anomaly Detection in a Physical Model of a Water Supply System   (2021)
Journal Article
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., & Tan, Z. (2021). Newly Engineered Energy-based Features for Supervised Anomaly Detection in a Physical Model of a Water Supply System  . Ad hoc networks, 120, Article 102590. https://doi.org/10.1016/j.adhoc.2021.102590

Industrial Control Systems (ICS) are hardware, network, and software, upon which a facility depends to allow daily operations to function. In most cases society takes the operation of such systems, for example public transport, tap water or electrici... Read More about Newly Engineered Energy-based Features for Supervised Anomaly Detection in a Physical Model of a Water Supply System  .

Wiedziec wiecej Internet (1997)
Book
Buchanan, W. J. (1997). Wiedziec wiecej Internet. Wkt (Poland)

Ksi??ka ta m. In. Uczy jak korzysta? z internetu i sieci www.Opisuje bowiem techniczne aspekty tej ?wiatowej sieci informacyjnej. Dlatego te? mo?e by? wykorzystywana przez wszystkich uczniów i studentów, którzy nie tylko chc? korzysta? z internetu, l... Read More about Wiedziec wiecej Internet.

A Comparative Analysis of Honeypots on Different Cloud Platforms (2021)
Journal Article
Kelly, C., Pitropakis, N., Mylonas, A., McKeown, S., & Buchanan, W. J. (2021). A Comparative Analysis of Honeypots on Different Cloud Platforms. Sensors, 21(7), Article 2433. https://doi.org/10.3390/s21072433

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to special... Read More about A Comparative Analysis of Honeypots on Different Cloud Platforms.

Privacy and Trust Redefined in Federated Machine Learning (2021)
Journal Article
Papadopoulos, P., Abramson, W., Hall, A. J., Pitropakis, N., & Buchanan, W. J. (2021). Privacy and Trust Redefined in Federated Machine Learning. Machine Learning and Knowledge Extraction, 3(2), 333-356. https://doi.org/10.3390/make3020017

A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited... Read More about Privacy and Trust Redefined in Federated Machine Learning.

Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks (2021)
Journal Article
Alsarhan, A., Alauthman, M., Alshdaifat, E., Al-Ghuwairi, A., & Al-Dubai, A. (2023). Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks. Journal of Ambient Intelligence and Humanized Computing, 14(5), 6113-6122. https://doi.org/10.1007/s12652-021-02963-x

Machine Learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VAN... Read More about Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks.

Prediction of electric fields in and around PCBs — 3D finite-difference time-domain approach with parallel processing (1995)
Journal Article
Buchanan, W. J., & Gupta, N. K. (1995). Prediction of electric fields in and around PCBs — 3D finite-difference time-domain approach with parallel processing. Advances in engineering software, 23(2), 111-114. https://doi.org/10.1016/0965-9978%2895%2900068-8

The authors have taken the 3D FDTD approach to simulate the propagation of electrical signals within and around printed circuit boards (PCBs). This relates to the work currently being carried out into the propagation of very high speed digital pulses... Read More about Prediction of electric fields in and around PCBs — 3D finite-difference time-domain approach with parallel processing.

An experimental analysis of attack classification using machine learning in IoT networks (2021)
Journal Article
Churcher, A., Ullah, R., Ahmad, J., Ur Rehman, S., Masood, F., Gogate, M., Alqahtani, F., Nour, B., & Buchanan, W. J. (2021). An experimental analysis of attack classification using machine learning in IoT networks. Sensors, 21(2), Article 446. https://doi.org/10.3390/s21020446

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature,... Read More about An experimental analysis of attack classification using machine learning in IoT networks.

Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach (2020)
Presentation / Conference Contribution
Christou, O., Pitropakis, N., Papadopoulos, P., Mckeown, S., & Buchanan, W. J. (2020, February). Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach. Presented at ICISSP 2020, Valletta, Malta

Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. Even with adequate training and high situational awareness, it can still be hard for users to continually be awa... Read More about Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach.

Privacy-preserving Surveillance Methods using Homomorphic Encryption (2020)
Presentation / Conference Contribution
Bowditch, W., Abramson, W., Buchanan, W. J., Pitropakis, N., & Hall, A. J. (2020, February). Privacy-preserving Surveillance Methods using Homomorphic Encryption. Presented at 6th International Conference on Information Security Systems and Privacy (ICISSP), Valletta, Malta

Data analysis and machine learning methods often involve the processing of cleartext data, and where this could breach the rights to privacy. Increasingly, we must use encryption to protect all states of the data: in-transit, at-rest, and in-memory.... Read More about Privacy-preserving Surveillance Methods using Homomorphic Encryption.

Understanding Personal Online Risk To Individuals Via Ontology Development (2020)
Presentation / Conference Contribution
Haynes, D. (2020, July). Understanding Personal Online Risk To Individuals Via Ontology Development. Presented at International Societey for Knowledge Organziation (ISKO) 2020, Aalborg, Denmark

The concept of risk is widely misunderstood because of the different contexts in which it is used. This paper describes the development of an ontology of risk as a way of better understanding the nature of the potential harms individuals are exposed... Read More about Understanding Personal Online Risk To Individuals Via Ontology Development.

Review and Critical Analysis of Privacy-preserving Infection Tracking and Contact Tracing (2020)
Journal Article
Buchanan, W. J., Imran, M. A., Ur-Rehman, M., Zhang, L., Abbasi, Q. H., Chrysoulas, C., Haynes, D., Pitropakis, N., & Papadopoulos, P. (2020). Review and Critical Analysis of Privacy-preserving Infection Tracking and Contact Tracing. Frontiers in Communications and Networks, https://doi.org/10.3389/frcmn.2020.583376

The outbreak of viruses have necessitated contact tracing and infection tracking methods. Despite various efforts, there is currently no standard scheme for the tracing and tracking. Many nations of the world have therefore, developed their own ways... Read More about Review and Critical Analysis of Privacy-preserving Infection Tracking and Contact Tracing.

A Novel Web Attack Detection System for Internet of Things via Ensemble Classification (2020)
Journal Article
Luo, C., Tan, Z., Min, G., Gan, J., Shi, W., & Tian, Z. (2021). A Novel Web Attack Detection System for Internet of Things via Ensemble Classification. IEEE Transactions on Industrial Informatics, 17(8), 5810-5818. https://doi.org/10.1109/tii.2020.3038761

Internet of things (IoT) has become one of the fastestgrowing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that... Read More about A Novel Web Attack Detection System for Internet of Things via Ensemble Classification.

A Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
Presentation / Conference Contribution
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020, September). A Distributed Trust Framework for Privacy-Preserving Machine Learning. Presented at The 17th International Conference on Trust, Privacy and Security in Digital Business - TrustBus2020, Bratislava, Slovakia

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.

FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures (2020)
Journal Article
Popov, S., & Buchanan, W. J. (2021). FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures. Journal of Parallel and Distributed Computing, 147, 77-86. https://doi.org/10.1016/j.jpdc.2020.09.002

This paper presents a novel leaderless protocol (FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures) with a low communicational complexity and which allows a set of nodes to come to a consensus on a value of a single bit. The paper... Read More about FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures.