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VLTN focus on research and consultancy services in the utilisation of advanced ICT in new products and processes. Core competencies include Cloud-based stochastic simulation for predictive analytics, probabilistic/graph knowledge models for decision making and distributed and autonomous control, with applications in risk management and security, transport logistics and healthcare.

We utilise Cloud-based real time stohastic/predictive simulations in domains with large quantities of dynamically-changing data that must be analysed, understood and often, acted upon in real-time. With Cloud-based simulations, huge numbers of alternative scenarios can be generated and evaluated to support robust decision making processes in planning, decision making and risk mitigation. The accuracy and insights gained from the simulation results using our technique far surpass traditional what-if scenario analysis techniques.

Attack graph modelling and analysis for cyberphysical systems

One key area where we apply Cloud based stochastic simulations is for automating the construction of attack graphs of the cyberphysical system for analysis and response strategies. Our approach speeds up the graph construction process and automates the analysis. This yields a focused and relevant representation of the cyberphysical system, and also allows prioritizing the security properties whose violations are of greater concern, for both detection and repair. We also employ attack graph-based security metrics to improve hardening with the given budget constraints.

Multilayer Fog computing architectures for cyber-defence

We base our approach on principles of defence-in-depth, with layered security mechanisms to increase security of the system as a whole, as if an attack causes one security mechanism to fail, other mechanisms can still provide the necessary security protection. We believe that centralised security solutions are not as potent as security at the periphery. We design Fog computing security architectures, where lines of defence are directly deployed as close as possible to the IoT or other external networks. This approach ensures low latency but also sufficient local ‘firepower’ for effective cyber-defence.

Security and Risk Intelligence Gathering using Web scale graph analytics

An essential part of effective cyber-defence is intelligence gathering that leads to more accurate vulnerability analysis and from there to defence preparation. However, scanning the entire Internet/Web for intelligence gathering requires effort only afforded by the largest security agencies. Our solution to this problem is special purpose knowledge graphs that represent only the most relevant from a security/risk angle links and relations between entities that have a web presence. This allows the automated construction and replay of scenarios that use the strength of relations to understand existing risk (business risk, physical or cybersecurity risk) and calculate the most relevant (e.g. likely or high impact) risk scenarios.
 
 
 
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