Sam Maesschalck – Systems & Security Group (SSG)

I’m Sam, a PhD student under Dr Vasileios Giotsas, Prof Nicholas Race and Dr Benjamin Green. My current research involves the use of honeypots for industrial control system security and is funded by the NG-CDI project.

Contact information: s.maesschalck@lancaster.ac.uk

Research Interests

My research interests mainly lie within network security and digital forensics. These research areas are further underlined by an interest for honeypots, industrial control systems and software-defined-networking. Due to the nature of industrial control systems, a need for specific security measures has emerged, specifically one that has minimal interference with the devices itself.

Further interests lie within the aspect of cyberspace and its effect on national security and international relations. The emergence of cyberspace as a new domain of warfare has brought many questions with it and exposes nations to a range of new threats.

Projects

The NG-CDI project is an ambitious programme geared to create a radically new architecture for the UK’s internet and telecommunications infrastructure. It is a collaborative partnership with the Universities of Lancaster, Bristol, Cambridge and Surrey and BT

News & Events
IEEE TFS’24 Y. Li et al, Adversarial Attack Detection via Fuzzy Predictions. pdf
ECCV’24 Y. Li et al, Self-Supervised Representation Learning for Adversarial Attack Detection. pdf
ANRW '24 S. Kastanakis et al, Investigating Location-Aware Advertisements in Anycast IP Networks. pdf
IJCNN '24 Y. Li et al, Rethinking Self-Supervised Learning for Cross-Domain Adversarial Sample Recovery. pdf
ICANN '24 Y. Li et al, Federated Adversarial Learning for Robust Autonomous Landing Runway Detection. pdf
IJCNN '24 A. Pellcier et al, UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification. pdf
NFM '24 O. Gunasekera et al, Real Arithmetic in TLAPM. pdf
IEEE TCC S. Manzoor et al, Enabling Multi-Layer Threat Analysis in Dynamic Cloud Environments. pdf
arXiv S. Winter et al, Error Propagation Analysis for Multithreaded Programs: An Empirical Approach. pdf
Springer Open’24 E. Chuah et al, An Empirical Study of Reflection Attacks Using NetFlow Data. pdf
Space Policy’23 V. Varadharajan et al, Security Challenges When Space Merges With Cyberspace. pdf
LANMAN’23 S. Rene et al, Scalable Content-centric Routing for Hybrid ICN. pdf
DAPPS’23 N. Keizer et al, Ditto: Towards Decentralised Similarity Search for Web3 Services. pdf
IMC’23 L. Balduf et al, The Cloud Strikes Back: Investigating the Decentralization of IPFS. pdf
CAMLIS’23 S. Trawicki et al, Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization. pdf
CAMLIS’23 L. Birch et al, Model Leeching: An Extraction Attack Targeting LLMs. pdf
SSCI’23 Y. Li et al, Fuzzy Detectors Against Adversarial Attacks. pdf
IMC’23 S. Kastanakis et al, 20 Years of Inferring Inter-domain Routing Policies. pdf
DASC’23 B. Yuksek et al, Federated Meta Learning for Visual Navigation in GPS-denied Urban Airspace. pdf
NDSS’24 S. Sridhar et al, Content Censorship in the InterPlanetary File System. pdf
MASS’23 Z. Yu et al, RAFL: A Robust and Adaptive Federated Meta-Learning Framework Against Adversaries. pdf
TOPS’23 Y. Lu et al, Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph. pdf
T-ASE’23 Y. Lu et al, Transactive Energy System Deployment over Insecure Communication Links. pdf
IJCNN’23 Y. Li et al, Domain Generalization and Feature Fusion for Cross-domain Imperceptible Adversarial Attack Detection. pdf
CyBok: SSG Prof. Suri defines NCSC’s CyBOK knowledge area on Distributed Systems Security
Prof. Suri to lead LU's ACE-CSE
Security Institute to lead £3M UKRI-TAS research node to examine cybersecurity in autonomous systems.^New
MSN'22 Best Paper Award for Z. Yu et al, PPFM: An Adaptive and Hierarchical Peer-to-Peer Federated Meta-Learning Framework
ICNC '23: A. Taha et al, Cloud Security Requirement Based Threat Analysis, to appear.
MSN'22: Z. Yu et al, PPFM: An Adaptive and Hierarchical Peer-to-Peer Federated Meta-Learning Framework. pdf
CIDUE/SSCI'22: E. Soares et al, Similarity-based Deep Neural Network to Detect Imperceptible Adversarial Attacks. pdf
ISSRE'22: Y. Chen et al, SlowCoach: Mutating Code to Simulate Performance Bugs. pdf
FGCS'22: M. Bradbury et al, Information Management for Trust Computation on Resource-constrained IoT Devices. pdf
ToSN'22: M. Bradbury et al, Threat Modelling Guided Trust-based Task Offloading for Resource-constrained Internet of Things. pdf
NCA'21: E. Chuah et al, Challenges in Identifying Network Attacks Using Netflow Data. pdf
COSE'21: B. Green et al, PCaaD: Towards automated determination and exploitation of industrial systems. pdf