Prodevelop (Spain)

In the era of digital transformation, the increasing vulnerability of infrastructure and applications is often tied to the lack of technical capability and the improved intelligence of attackers. In this paper, we discuss the complementarity between static security monitoring of rule matching and an application of self-supervised machine learning to cybersecurity. Moreover, we analyze the context and challenges of supply chain resilience and smart logistics. Furthermore, we put this interplay between the two complementary methods in the context of a self-learning and self-healing approach.

<table> <tbody> <tr> <td>This document reports the work conducted in the inter-pilot and collaboration task<br> (T7.6). It includes new use cases tested in each pilot site, describes the main<br> generalization aspects of the PIXEL Platform and models and lessons learnt<br> regarding the PIXEL framework, in the four use-cases, have been included.</td> </tr> </tbody> </table> <p> </p>