Biography
Martin is a researcher on the AMIDiNe project investigating energy forecasting for industrial parks. He joins the e-research centre from Imperial College where he completed his PhD. At Imperial, Martin’s focus was in the field of cyber-security for power systems. He published 3 papers in this area with a specialisation on false data injection attacks against measurement systems and moving target style defences.
Martin is also a board member and co-founder of the Royal-Imperial Black Box (RIBB). The RIBB is an advanced analytics platform for enhanced industrial control system cyber security and received over 80K GDP pre-seed in funding from InnovateUK. Martin has a passion for power systems, cyber-security, energy policy, BJJ, and AFC Bournemouth.
Most Recent Publications
Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection
Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection
Research Interests
• Energy forecasting to assist with network planning
• Anomaly detection and data-driven detection of attacks against networked systems
• False data attacks against networked systems these include power networks, gas networks, water networks or even social networks.
• Moving Target Defences i.e. how networked systems can be used to protect themselves
Current Projects
AMINDE Project - Forecasting of energy load consumption from industrial parks.
Research Groups
Related Academics
Most Recent Publications
Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection
Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection
Most Recent Publications
Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection
Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection
