A computer-implemented method for hybrid consensus in blockchain networks, the method being executed by one or more processors and comprising: providing a security rating and a data criticality value of one or more transactions, the security rating depending on a value and a criticality of the one or more transactions, the data criticality value representing a relative importance of data of the one or more transactions, the one or more transactions to be recorded to a blockchain , and the blockchain being of a blockchain network ; selecting a consensus protocol, the consensus protocol selected from a set of consensus protocols, and the consensus protocol selected based on the security rating and the data criticality value by a machine learning, ML, model that receives the security rating and the data criticality value as input; defining a set of consensus nodes , the set of consensus nodes comprising: nodes from one of: a super node pool, and a weak node pool; providing a weight assigned to the consensus protocol based on the security rating and the data criticality value, wherein the weight defines a threshold number of consensus nodes in the set of consensus nodes that have to validate the one or more transactions to record the one or more transactions to the blockchain; and executing, by the set of consensus nodes and based on the threshold number of consensus nodes in the set of consensus nodes that have to validate the one or more transactions to record the one or more transactions to the blockchain, the consensus protocol to record the one or more transactions to the blockchain. providing a security rating and a data criticality value of one or more transactions, the security rating depending on a value and a criticality of the one or more transactions, the data criticality value representing a relative importance of data of the one or more transactions, the one or more transactions to be recorded to a blockchain , and the blockchain being of a blockchain network ; the security rating depending on a value and a criticality of the one or more transactions, the data criticality value representing a relative importance of data of the one or more transactions, the one or more transactions to be recorded to a blockchain , and the blockchain being of a blockchain network ; selecting a consensus protocol, the consensus protocol selected from a set of consensus protocols, and the consensus protocol selected based on the security rating and the data criticality value by a machine learning, ML, model that receives the security rating and the data criticality value as input; the consensus protocol selected from a set of consensus protocols, and the consensus protocol selected based on the security rating and the data criticality value by a machine learning, ML, model that receives the security rating and the data criticality value as input; defining a set of consensus nodes , the set of consensus nodes comprising: nodes from one of: a super node pool, and a weak node pool; a super node pool, and a weak node pool; providing a weight assigned to the consensus protocol based on the security rating and the data criticality value, wherein the weight defines a threshold number of consensus nodes in the set of consensus nodes that have to validate the one or more transactions to record the one or more transactions to the blockchain; and executing, by the set of consensus nodes and based on the threshold number of consensus nodes in the set of consensus nodes that have to validate the one or more transactions to record the one or more transactions to the blockchain, the consensus protocol to record the one or more transactions to the blockchain. <EOS>