Attack detection from models using machine learning

The PhD project tackles runtime detection of cyber attacks. It combines security by design with runtime security checking by using design models for runtime attack detection. Model2Defend proposes research on: (i) graphical design languages with a trace semantics to express security models, and (ii) an attack detector that uses those models and machine learning (ML). Model2Defend aims to give security analysts the means to: (i) security analyse systems at design-time, (ii) represent knowledge about possibly ongoing security threats and (iii) actively defend against threats and attacks.

More information on the project, from potential impact to references, can be found on the accompanying PDF.

To apply, please complete the project proposal form and the online application.

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