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This work presents the results of the DETECTA project, which addressesindustrial research activities for the generation of predictive knowledge aimedat detecting anomalies in machining-based manufacturing systems. It addressesdifferent technological challenges to simultaneously improve the availabilityof machinery and the protection against cyberthreats of industrial systems,with the collaboration of knowledge centers and experts in industrialprocesses. Through the use of innovative technologies such as the digital twinand artificial intelligence, it implements process characterizationmethodologies and anomaly detection in a non-intrusive way without limiting theproductivity of the industrial plant according to the maintenance and remoteaccess needs. The research has been supported by a general evaluation ofconnected environments in small and medium-sized enterprises to identify if thebenefits of digitization outweigh the risks that cannot be eliminated. Theresults obtained, through a process of supervision by process experts andmachine learning, have made it possible to discriminate anomalies betweenpurely technical events and events related to cyber incidents or cyber attacks.

DETECTA: Investigaci\'on de metodolog\'ias no intrusivas apoyadas en  tecnolog\'ias habilitadoras 4.0 para abordar un mantenimiento predictivo y  ciberseguro en pymes industriales