1. A computer-implemented method for processing streams of information security data from one or more networked computer systems, the method comprising: receiving, by a machine learning engine executing on one or more computing systems, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network; generating a neuro-linguistic model of the information security data by: clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words; evaluating a current observation of letters, words, or phrases generated from one or more subsequent normalized vectors received from the ordered stream input to the neuro-linguistic model to determine a measure of unusualness for the current observation of letters, words, or phrases; generating an alert when the measure of unusualness determined for the current observation of letters, words, or phrases exceeds a specified threshold, wherein the alert corresponds to activity occurring within the computer network which resulted in the current observation of letters, words, or phrases; transmitting the alert to a management console; and dynamically updating the neuro-linguistic model based on the one or more subsequent normalized vectors from the ordered stream input to the neuro-linguistic model.
5. The method of claim 1 , wherein building the dictionary comprises building a dictionary of words up to a maximum length of letters.