Research Programme

Knowledge-driven Cyber-Physical Security at the Edge

(2021-2025)

Security Knowledge Graphs

Internet of Things (IoT) Infrastructure / Systems (IS) Security (S) Knowledge Representation (KR)

Project Overview

Uses knowledge-based modelling and the MAPE-K loop to orchestrate self-adaptive cyber-physical security for smart homes without relying on the cloud.

This research develops self-adaptive security techniques for heterogeneous smart home environments using edge computing rather than cloud services. Smart devices are heterogeneous where each of them has a different set of capabilities in terms of sensing and actuation. The project employs the MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) methodology to dynamically configure smart spaces while addressing device diversity, resource constraints, and contextual changes.

The project captures device capabilities, context, and policies in a knowledge base and applies the MAPE-K loop to plan and execute protective actions locally. By combining rule engines and AI planners with open-source frameworks such as Drools, OpenHAB, and Optaplanner, the system adapts to new conditions while keeping data on the edge.

Research Objectives:

Conduct a comprehensive literature review on knowledge-based techniques for smart home cyber-physical security
Develop knowledge models supporting the MAPE-K loop
Investigate and implement techniques using open-source frameworks
Evaluate trade-offs between competing approaches
Create demonstrators showcasing knowledge-based self-adaptive systems