1. A method for training a big data machine to defend an enterprise system comprising: retrieving log lines belonging to one or more log line parameters from one or more enterprise system data sources and from incoming data traffic to the enterprise system; computing one or more features from the log lines; wherein computing one or more features includes one or more statistical processes; applying the one or more features to an adaptive rules model; wherein the adaptive rules model comprises one or more identified threat labels; further wherein applying the one or more features to an adaptive rules model comprises: blocking one or more features that has one or more identified threat labels; generating a features matrix from said applying the one or more features to an adaptive rule module; executing at least one detection method from a first group of statistical outlier detection methods and at least one detection method from a second group of statistical outlier detection methods on one or more features matrix, to identify statistical outliers; wherein the first group of statistical outlier detection methods includes a matrix decomposition-based outlier process, a replicator neural networks process and a joint probability process and the second group of statistical outlier detection methods includes a matrix decomposition-based outlier process, a replicator neural networks process and a joint probability process; wherein the at least one detection method from a first group of statistical outlier detection methods and the at least one detection method from a second group of statistical outlier detection methods are different; generating an outlier scores matrix from each detection method of said first and second group of statistical outlier detection methods; converting each outlier scores matrix to a top scores model; combining each top scores model using a probability model to create a single top scores vector; generating a GUI output of at least one of: an output of the single top scores vector and the adaptive rules model; labeling the said output to create one or more labeled features matrix; creating a supervised learning module with the one or more labeled features matrix to update the one or more identified threat labels for performing at least one of: further refining adaptive rules model for identification of statistical outliers; and preventing access by categorized threats by detecting new threats in real time and reducing the time elapsed between threat detection of the enterprise system.