Abstract:
The discovery of essential entities behind an APT move on a platform or which play a preponderant role in the dynamics of a network flow is a crucial objective in the analysis of complex systems’ security. To achieve this objective, we propose the use of flow variability and travel time measures as well as dynamic path congestion factors to enrich centrality measures in complex networks. Integrating network dynamics requires the collection and management of big data from various sources, especially from recorded datasets used upon historical APTs[1, 2, 6]. The concept of adequate data flow sampling has been integrated into the proposed smart environment. We also present the big data architecture, as well as the orchestration of software components, both for data capture, generation of congestion events, calculation of time centralities, analytics and visualization. The built solution constitutes a fundamental building block in macro-regulation and traffic monitoring in work and business environments.
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