141 citations · 513 across the 18 of their papers we have counts for
7 papers · 1 filter
Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers
Hisham Ali, Pavlos Papadopoulos, Jawad Ahmad +3
Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can prov…
Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure
Edward Henry Young, Christos Chrysoulas, Nikolaos Pitropakis +2
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 effec…
GLASS: Towards Secure and Decentralized eGovernance Services using IPFS
Christos Chrysoulas, Amanda Thomson, Nikolaos Pitropakis +7
The continuously advancing digitization has provided answers to the bureaucratic problems faced by eGovernance services. This innovation led them to an era of automation it has bro…
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
Pavlos Papadopoulos, Oliver Thornewill von Essen, Nikolaos Pitropakis +3
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 Interne…
Practical Defences Against Model Inversion Attacks for Split Neural Networks
Tom Titcombe, Adam J. Hall, Pavlos Papadopoulos +1
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 demonstrat…
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
Daniele Romanini, Adam James Hall, Pavlos Papadopoulos +6
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…
Privacy and Trust Redefined in Federated Machine Learning
Pavlos Papadopoulos, Will Abramson, Adam J. Hall +2
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 r…