4. The method of claim 1 , wherein labeling the output further includes classifying the severity of the threat.
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 the 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 the adaptive rules model; 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 the first group of statistical outlier detection methods and the at least one detection method from the 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 (Graphical User Interface) 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 the 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.