Outputs (433)

Practical defences against model inversion attacks for split neural networks (2021)
Presentation / Conference Contribution
Titcombe, T., Hall, A. J., Papadopoulos, P., & Romanini, D. (2021, May). Practical defences against model inversion attacks for split neural networks. Paper presented at ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021), Online

We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server. We demonstrate that the attack can be successfully performed with limited knowledge... Read More about Practical defences against model inversion attacks for split neural networks.

PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN (2021)
Presentation / Conference Contribution
Romanini, D., Hall, A. J., Papadopoulos, P., Titcombe, T., Ismail, A., Cebere, T., Sandmann, R., Roehm, R., & Hoeh, M. A. (2021, May). PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN. Poster presented at ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021), Online

We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keep... Read More about PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN.

Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT (2021)
Journal Article
Papadopoulos, P., Thornewill Von Essen, O., Pitropakis, N., Chrysoulas, C., Mylonas, A., & Buchanan, W. J. (2021). Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT. Journal of Cybersecurity and Privacy, 1(2), 252-273. https://doi.org/10.3390/jcp1020014

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defe... Read More about Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT.

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.-R., & 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.

The reality of 'cyber security awareness': findings and policy implications for Scotland (2021)
Report
Horgan, S. (2021). The reality of 'cyber security awareness': findings and policy implications for Scotland. Scottish Centre for Crime and Justice Research, the Scottish Institute for Policing Research, and the Scottish Government

This briefing paper represents a summary of doctoral research that explores how different groups make sense of and respond to cybercrime in their everyday lives. The research found that people from different groups, places, and times think about cybe... Read More about The reality of 'cyber security awareness': findings and policy implications for Scotland.

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.

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. (online). 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.