Outputs (44)

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.

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.

Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning (2020)
Presentation / Conference Contribution
Angelou, N., Benaissa, A., Cebere, B., Clark, W., Hall, A. J., Hoeh, M. A., Liu, D., Papadopoulos, P., Roehm, R., Sandmann, R., Schoppmann, P., & Titcombe, T. (2020, December). Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning. Poster presented at NeurIPS 2020 Workshop on Privacy Preserving Machine Learning (PPML 2020), Online

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters th... Read More about Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning.

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.

PoNW: A Secure and Scalable Proof-of-Notarized-Work Based Consensus Mechanism (2020)
Presentation / Conference Contribution
Abubakar, M., Jaroucheh, Z., Al-Dubai, A., & Buchanan, W. (2020, December). PoNW: A Secure and Scalable Proof-of-Notarized-Work Based Consensus Mechanism. Presented at ICVISP 2020: The 2020 4th International Conference on Vision, Image and Signal Processing, Bangkok

The original consensus algorithm-Proof of Work (PoW) has been widely utilized in the blockchain systems and is been adopted by many cryptocurrencies, such as Bitcoin and Ethereum, among many others. Nevertheless, the concept has received criticisms o... Read More about PoNW: A Secure and Scalable Proof-of-Notarized-Work Based Consensus Mechanism.

A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric (2020)
Journal Article
Stamatellis, C., Papadopoulos, P., Pitropakis, N., Katsikas, S., & Buchanan, W. J. (2020). A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric. Sensors, 20(22), Article 6587. https://doi.org/10.3390/s20226587

Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches often face risks that may expose medical record storage solutions to common sec... Read More about A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric.

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.

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.

Fast Probabilistic Consensus with Weighted Votes (2020)
Presentation / Conference Contribution
Müller, S., Penzkofer, A., Ku´smierz, B., Camargo, D., & Buchanan, W. J. (2020, November). Fast Probabilistic Consensus with Weighted Votes. Presented at FTC 2020 - Future Technologies Conference 2020, Vancouver, Canada

The fast probabilistic consensus (FPC) is a voting consensus protocol that is robust and efficient in Byzantine infrastructure. We propose an adaption of the FPC to a setting where the voting power is proportional to the nodes reputations. We model t... Read More about Fast Probabilistic Consensus with Weighted Votes.

Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection (2020)
Journal Article
Tian, Z., Shi, W., Tan, Z., Qiu, J., Sun, Y., Jiang, F., & Liu, Y. (in press). Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection. Mobile Networks and Applications, https://doi.org/10.1007/s11036-020-01656-7

Organizations' own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for ma... Read More about Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection.

BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond (2020)
Journal Article
Xu, H., Zhang, L., Onireti, O., Fang, Y., Buchanan, W. J., & Imran, M. A. (2021). BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond. IEEE Internet of Things, 8(5), 3915-3929. https://doi.org/10.1109/jiot.2020.3025953

The outbreak of COVID-19 pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on p... Read More about BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond.

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.

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.

DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption (2020)
Journal Article
Khan, J. S., Boulila, W., Ahmad, J., Rubaiee, S., Rehman, A. U., Alroobaea, R., & Buchanan, W. J. (2020). DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption. IEEE Access, 8, 159732-159744. https://doi.org/10.1109/access.2020.3020917

Visual selective image encryption can both improve the efficiency of the image encryption algorithm and reduce the frequency and severity of attacks against data. In this article, a new form of encryption is proposed based on keys derived from Deoxyri... Read More about DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption.

Evaluation of Ensemble Learning for Android Malware Family Identification (2020)
Journal Article
Wylie, J., Tan, Z., Al-Dubai, A., & Wang, J. (2020). Evaluation of Ensemble Learning for Android Malware Family Identification. Journal of Guangzhou University (Natural Science Edition), 19(4), 28-41

Every Android malware sample generally belongs to a specific family that performs a similar set of actions and characteristics. Having the ability to effectively identify Android malware families can assist in addressing the damage caused by malware.... Read More about Evaluation of Ensemble Learning for Android Malware Family Identification.

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment (2020)
Journal Article
Zhao, L., Huang, H., Su, C., Ding, S., Huang, H., Tan, Z., & Li, Z. (2021). Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment. IEEE Internet of Things Journal, 8(5), 3211-3223. https://doi.org/10.1109/jiot.2020.3019732

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion de... Read More about Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment.

FPDP: Flexible Privacy-preserving Data Publishing Scheme for Smart Agriculture (2020)
Journal Article
Song, J., Zhong, Q., Su, C., Tan, Z., & Liu, Y. (2021). FPDP: Flexible Privacy-preserving Data Publishing Scheme for Smart Agriculture. IEEE Sensors Journal, 21(16), 17430-17438. https://doi.org/10.1109/JSEN.2020.3017695

Food security is a global concern. Benefit from the development of 5G, IoT is used in agriculture to help the farmers to maintain and improve productivity. It not only enables the customers, both at home and abroad, to become more informed about the... Read More about FPDP: Flexible Privacy-preserving Data Publishing Scheme for Smart Agriculture.

Trust-based Ecosystem to Combat Fake News (2020)
Presentation / Conference Contribution
Jaroucheh, Z., Alissa, M., & Buchanan, W. J. (2020, May). Trust-based Ecosystem to Combat Fake News. Presented at 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada

The growing spread of misinformation and dis-information has grave political, social, ethical, and privacy implications for society. Therefore, there is an ethical need to combat the flow of fake news. This paper attempts to resolves some of the aspe... Read More about Trust-based Ecosystem to Combat Fake News.