Series of utilities and training models for network security. Built in Scala for Apache Spark MLLib for learning purposes

Logistic Regression prediction model for IDS Logs.

This was trained with an updated version of the KDD dataset that everyone uses.

Linear Regression prediction model for server health data.

This is a demo, but if given real data it yields a model that can be worked with. Currently the label is based upon the cpu load average, but this can be modified to other things, as shown below.

// Use memory utilization as the label
data.select(data("memutil")
    .as("label"),$"hour",$"load",$"netutil")

Additionally datasets can be filtered by modifying the select on the dataframe, as shown below

// Train only on data from the abstract host taken at noon
data.filter("hostname = 'abstract' AND hour = 12")
    .select(data("load")
    .as("label"),$"hour",$"memutil",$"netutil")

util/health-log-gen.py will generate some csv logs to train on, however this data is randomized and won't yield any worthwhile results. util/network-log-gen.py will generate some JSON logs to train on.