A computer implemented method of a network component to identify a computer security threat based on communication of a network connected device via a computer network, characterised in that the method comprises: receiving a plurality of blocks of network traffic from the device, each block including a sequence of network traffic data items being identifiable by a position in the sequence of the block; identifying a subset of positions occurring in every block for which a degree of variability of values of data items in each position of the subset meets a predetermined threshold by using a machine learning algorithm to identify positions in every block at which data items exhibit at least a predetermined degree of consistency, wherein the machine learning algorithm is an unsupervised algorithm such as an autoencoder; generating, by a validator generator component of the network component, executable code for performing a plurality of processing operations based on the identified subset of positions, the executable code consuming a determinate quantity of computing resources when executed for the received network traffic, wherein the executable code is executable by a validator executer component of the network component to detecting a subsequent network communication as a block of network traffic having a sequence of data items for which the identified subset of positions fails to exhibit a degree of variability meeting the predetermined threshold, the detection being based on a comparison of a measure of resources consumed by a computer system executing the executable code and he determinate quantity of computing resources, and the detection corresponding to the identification of a computer security threat. receiving a plurality of blocks of network traffic from the device, each block including a sequence of network traffic data items being identifiable by a position in the sequence of the block; identifying a subset of positions occurring in every block for which a degree of variability of values of data items in each position of the subset meets a predetermined threshold by using a machine learning algorithm to identify positions in every block at which data items exhibit at least a predetermined degree of consistency, wherein the machine learning algorithm is an unsupervised algorithm such as an autoencoder; generating, by a validator generator component of the network component, executable code for performing a plurality of processing operations based on the identified subset of positions, the executable code consuming a determinate quantity of computing resources when executed for the received network traffic, wherein the executable code is executable by a validator executer component of the network component to detecting a subsequent network communication as a block of network traffic having a sequence of data items for which the identified subset of positions fails to exhibit a degree of variability meeting the predetermined threshold, the detection being based on a comparison of a measure of resources consumed by a computer system executing the executable code and he determinate quantity of computing resources, and the detection corresponding to the identification of a computer security threat.