Abstract

Interoperability among heterogeneous systems is a key chal-lenge in today’s networked environment, which is charac-terised by continual change in aspects such as mobility and availability. Automated solutions appear then to be the only way to achieve interoperability with the needed level of flex-ibility and scalability. While necessary, the techniques used to achieve interaction, working from the highest application level to the lowest protocol level, come at a substantial com-putational cost, especially when checks are performed in-discriminately between systems in unrelated domains. To overcome this, we propose to use machine learning to ex-tract the high-level functionality of a system and thus re-strict the scope of detailed analysis to systems likely to be able to interoperate. 1.