Security analytics framework validation based on threat intelligence

Logical analysis of the ontology of digital security in banking helps us to identify the possible entry points for illegal access. The threats described in the ontology are detected by Machine Learning engines. The theoretical analysis is validated by verifying the framework and Machine Learning algorithms. Intelligence Graphs (original term) which are adding the actions to knowledge graphs to form workflows, are a base for validation of the framework through simulated execution of the scenarios specified in them. The output is a method for analysing live network traffic data (machine learning algorithm) combined with semantic model to give a hybrid framework for threat intelligence in digital banking, leading to a complete threat detection platform. To prove our concept, we first devised an analytical validation scheme based on scenarios, which proves the viability of the premise, and then we implemented some scenarios, which demonstrate it in practice. The model is validated using operation workflows, namely 12 scenarios of banking “journeys” under the duress of various threats. In this work we are presenting the validation of the framework by simulation of the banking operations and transactions stemming from the Ontology of Digital Banking used as a model of the banking infrastructure (assets, vulnerabilities, and threats included). This model has been approved by the members of Lloyds Bank Cyber Security Division