Creating a learning model
The aim of this interface is to setup an anomaly detection system in real time.
To achieve this, the first thing needed is to save a training dataset from the log manager interface.
This dataset must reflects the most possible legitimate activities of an application, the goal being to be able to detect illegitimate activities.
Once the dataset saved, this last appears in the "Datasets" tab of the GUI.
In this interface, you can preview the learning bounds linked to you learning dataset. The bounds delimit area considered as legitimate.
6 algorithms are available:
Data received by server: Correlates the quantity if received response by the server and the HTTP status code.
Data sent by user: Correlates the quantity of sent data by the client and the HTTP status code.
Traffic evolution over day per IP: Correlates the number of requests per second per unique IP in terms of the time of the day.
Request content analysis: Analyzes the content of the request and compares it with the learning dataset to correlate the "distance" between words. Two sub algorithms are available, the first one analyze the whole request, the second split character by "/"
Once the real time button is ticked, the real time generated traffic on the application is analyzed and synchonized on the graphs. This let you check if the learning model works. If the analysis results are not compliant with reality, you have the ability to generate the model with different parameters. The traffic flagged as legitimate is represented as squares and the anomalies as dots.
On this preview interface, the "Map" tab let you preview the logs geographical source of the learning dataset. A filter by HTTP status code is available to let you check status codes generated by learning dataset logs.