All Outputs (24)

Explainable AI-Based DDOS Attack Identification Method for IoT Networks (2023)
Journal Article
Kalutharage, C. S., Liu, X., Chrysoulas, C., Pitropakis, N., & Papadopoulos, P. (2023). Explainable AI-Based DDOS Attack Identification Method for IoT Networks. Computers, 12(2), Article 32. https://doi.org/10.3390/computers12020032

The modern digitized world is mainly dependent on online services. The availability of online systems continues to be seriously challenged by distributed denial of service (DDoS) attacks. The challenge in mitigating attacks is not limited to identify... Read More about Explainable AI-Based DDOS Attack Identification Method for IoT Networks.

Towards The Creation Of The Future Fish Farm (2023)
Journal Article
Papadopoulos, P., Buchanan, W. J., Sayeed, S., & Pitropakis, N. (2023). Towards The Creation Of The Future Fish Farm. Journal of Surveillance, Security and Safety, 4, 1-3. https://doi.org/10.20517/jsss.2022.16

Aim: A fish farm is an area where fish raise and bred for food. Fish farm environments support the care and management of seafood within a controlled environment. Over the past few decades, there has been a remarkable increase in the calorie intake o... Read More about Towards The Creation Of The Future Fish Farm.

Investigating Machine Learning Attacks on Financial Time Series Models (2022)
Journal Article
Gallagher, M., Pitropakis, N., Chrysoulas, C., Papadopoulos, P., Mylonas, A., & Katsikas, S. (2022). Investigating Machine Learning Attacks on Financial Time Series Models. Computers and Security, 123, https://doi.org/10.1016/j.cose.2022.102933

Machine learning and Artificial Intelligence (AI) already support human decision-making and complement professional roles, and are expected in the future to be sufficiently trusted to make autonomous decisions. To trust AI systems with such tasks, a... Read More about Investigating Machine Learning Attacks on Financial Time Series Models.

Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform (2022)
Journal Article
Ali, H., Ahmad, J., Jaroucheh, Z., Papadopoulos, P., Pitropakis, N., Lo, O., …Buchanan, W. J. (2022). Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform. Entropy, 24(10), Article 1379. https://doi.org/10.3390/e24101379

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and impro... Read More about Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform.

Privacy-preserving systems around security, trust and identity (2022)
Thesis
Papadopoulos, P. Privacy-preserving systems around security, trust and identity. (Thesis). Edinburgh Napier University

Data has proved to be the most valuable asset in a modern world of rapidly advancing technologies. Companies are trying to maximise their profits by getting valuable insights from collected data about people’s trends and behaviour which often can be... Read More about Privacy-preserving systems around security, trust and identity.

GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture (2022)
Journal Article
Lo, O., Buchanan, W., Sayeed, S., Papadopoulos, P., Pitropakis, N., & Chrysoulas, C. (2022). GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture. Sensors, 22(6), Article 2291. https://doi.org/10.3390/s22062291

E-governance is a process that aims to enhance a government’s ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the esta... Read More about GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture.

Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems (2022)
Conference Proceeding
Grierson, S., Thomson, C., Papadopoulos, P., & Buchanan, B. (2022). Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems. In 2021 14th International Conference on Security of Information and Networks (SIN). https://doi.org/10.1109/sin54109.2021.9699157

Intrusion detection systems are integral to the security of networked systems for detecting malicious or anomalous network traffic. As traditional approaches are becoming less effective, machine learning and deep learning-based intrusion detection sy... Read More about Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems.

Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers (2022)
Conference Proceeding
Ali, H., Papadopoulos, P., Ahmad, J., Pit, N., Jaroucheh, Z., & Buchanan, W. J. (2022). Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers. In IEEE SINCONF: 14th International Conference on Security of Information and Networks. https://doi.org/10.1109/SIN54109.2021.9699366

Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the... Read More about Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers.

GLASS: Towards Secure and Decentralized eGovernance Services using IPFS (2022)
Conference Proceeding
Chrysoulas, C., Thomson, A., Pitropakis, N., Papadopoulos, P., Lo, O., Buchanan, W. J., …Tsolis, D. (2022). GLASS: Towards Secure and Decentralized eGovernance Services using IPFS. In Computer Security. ESORICS 2021 International Workshops. https://doi.org/10.1007/978-3-030-95484-0_3

The continuously advancing digitization has provided answers to the bureaucratic problems faced by eGovernance services. This innovation led them to an era of automation, broadened the attack surface and made them a popular target for cyber attacks.... Read More about GLASS: Towards Secure and Decentralized eGovernance Services using IPFS.

Ransomware: Analysing the Impact on Windows Active Directory Domain Services (2022)
Journal Article
McDonald, G., Papadopoulos, P., Pitropakis, N., Ahmad, J., & Buchanan, W. J. (2022). Ransomware: Analysing the Impact on Windows Active Directory Domain Services. Sensors, 22(3), Article 953. https://doi.org/10.3390/s22030953

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to... Read More about Ransomware: Analysing the Impact on Windows Active Directory Domain Services.

Decentralised Privacy: A Distributed Ledger Approach (2021)
Book Chapter
Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2021). Decentralised Privacy: A Distributed Ledger Approach. In C. Mustansar Hussain, & P. Di Sia (Eds.), Handbook of Smart Materials, Technologies, and Devices (1-26). Cham: Springer. https://doi.org/10.1007/978-3-030-58675-1_58-1

Our world due to the technological progress became fast-paced and is constantly evolving, thus changing every single day. Consequently, the most valuable asset on earth is not gold or oil anymore but data. Big data companies try to take advantage of... Read More about Decentralised Privacy: A Distributed Ledger Approach.

Browsers’ Private Mode: Is It What We Were Promised? (2021)
Journal Article
Hughes, K., Papadopoulos, P., Pitropakis, N., Smales, A., Ahmad, J., & Buchanan, W. J. (2021). Browsers’ Private Mode: Is It What We Were Promised?. Computers, 10(12), Article 165. https://doi.org/10.3390/computers10120165

Web browsers are one of the most used applications on every computational device in our days. Hence, they play a pivotal role in any forensic investigation and help determine if nefarious or suspicious activity has occurred on that device. Our study... Read More about Browsers’ Private Mode: Is It What We Were Promised?.

Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure (2021)
Presentation / Conference
Young, E. H., Chrysoulas, C., Pitropakis, N., Papadopoulos, P., & Buchanan, W. J. (2021, October). Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure. Paper presented at International Conference on Data Analytics for Business and Industry (ICDABI) 2021 - (DATA'21), Online

Little or no research has been directed to analysis and researching forensic analysis of the Bitcoin mixing or 'tumbling' service themselves. This work is intended to examine effective tooling and methodology for recovering forensic artifacts from tw... Read More about Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure.

PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform (2021)
Journal Article
Abramson, W., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2021). PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform. Software Impacts, 9, Article 100101. https://doi.org/10.1016/j.simpa.2021.100101

PyDentity lowers the entry barrier for parties interested in experimenting with the Hyperledger’s verifiable information exchange platform. It enables educators, developers and researchers to configure and initialise a set of actors easily as associa... Read More about PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform.

PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN (2021)
Presentation / Conference
Romanini, D., Hall, A. J., Papadopoulos, P., Titcombe, T., Ismail, A., Cebere, T., …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.

Practical defences against model inversion attacks for split neural networks (2021)
Presentation / Conference
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.

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.

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.

Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach (2020)
Conference Proceeding
Christou, O., Pitropakis, N., Papadopoulos, P., Mckeown, S., & Buchanan, W. J. (2020). Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach. In Proceedings of the 6th International Conference on Information Systems Security and Privacy (289-298). https://doi.org/10.5220/0008902202890298

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
Angelou, N., Benaissa, A., Cebere, B., Clark, W., Hall, A. J., Hoeh, M. A., …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.