Trust management based on dynamic supervised learning

A method comprising:

• obtaining one or more trained hidden Markov models for modelling behavior of respective one or more operational parameters of a device over time, wherein hidden states of the one or more trained hidden Markov models define expected future behavior of the respective one or more operational parameters and observation states of the one or more trained hidden Markov models define current states of the respective one or more operational parameters, the device being a cooperative intelligent transport systems, C-ITS, station or a mobile Internet of Things, IoT, device;
• receiving one or more messages from the device, wherein the one or more messages comprise values for the one or more operational parameters for a plurality of successive time instances corresponding to a pre-defined sliding time window;
• determining one or more sets of successive observation states based on the one or more messages, wherein the one or more sets relate, respectively, to the one or more operational parameters;
• determining one or more probability distributions of most probable paths through the one or more hidden Markov models using a Viterbi algorithm based on the one or more sets of successive observation states;
• determining one or more values of a trust rate based at least on an amount of dispersion in the one or more probability distributions;
• and
• classifying the device as trusted or untrusted based on the one or more values of the trust rate.

A non-transitory computer readable medium having stored thereon instructions that, when executed by a computing device, cause the computing device to perform:

• obtaining one or more trained hidden Markov models for modelling behavior of respective one or more operational parameters of a device over time, wherein hidden states of the one or more trained hidden Markov models define expected future behavior of the respective one or more operational parameters and observation states of the one or more trained hidden Markov models define current states of the respective one or more operational parameters, the device being a cooperative intelligent transport systems, C-ITS, station or a mobile Internet of Things, IoT, device;
• receiving one or more messages from the device, wherein the one or more messages comprise values for the one or more operational parameters for a plurality of successive time instances corresponding to a pre-defined sliding time window;
• determining one or more sets of successive observation states based on the one or more messages, wherein the one or more sets relate, respectively, to the one or more operational parameters;
• determining one or more probability distributions of most probable paths through the one or more hidden Markov models using a Viterbi algorithm based on the one or more sets of successive observation states;
• determining one or more values of a trust rate based at least on an amount of dispersion in the one or more probability distributions;
• and
• classifying the device as trusted or untrusted based on the one or more values of the trust rate.