When a sample is collected, the system generalized it into a pattern. For example, “abcd_123@abc.com” and “abcdefgecdf_12345678@efg.com” will both be generalized to the pattern “A_N@A.A”. The anomaly detection model is built based on the patterns, not the raw samples.

Extended: In Extended mode, it's required to also set the learning time. In extended mode at least 2500 samples will be collected and the sample collection period lasts for the specified weeks. For example, if you choose extended mode and set 1 week, the system stops collecting samples after 1 week if at least 2500 samples are collected by then, or continues collecting samples after 1 week until 2500 samples are collected.

Normal:In Normal mode, the system builds an initial model when the sample count reaches 400.

The system runs the initial model to detect anomalies, while it keeps collecting more samples to refine it.
Once the number of samples accumulates to 1200, the system will evaluate whether the patterns vary largely since the initial model is built.

• If there are very few patterns generalized, it indicates the patterns are stable. The system will switch the initial model to a standard model.
• If a lot of new patterns keeps coming in, the system will continue collecting more samples to cover as much patterns as possible. It won't switch to standard model until the patterns become stable.

The above mentioned numbers 400 and 1200 are configurable through CLI. Refer to config waf machine-learning-policy in FortiWeb CLI Reference.

Whether in extended or normal mode, the system keeps refining the model even after it's in running status.

Applications change frequently as new URLs are added and existing parameters provide new functions. This means the mathematical model of the same parameter might be different from what FortiWeb originally observed during the collection phase. In this case, FortiWeb needs to rebuild the mathematical model for the parameter.

Enable this option to automatically update the mathematical models of the parameters when they are changed.

HMM Parameter Model Update

This option appears when you enable Dynamically update when parameters change.

The system uses boxplots to determine whether a parameter has changed. The boxplot displays the probability distribution of the parameter value. During sample collection period, the system generates 2 or 4 boxplots. After anomaly detection model is built, the system will keep on generating new boxplots to display the probability distribution of the new inputs. If the probability distribution area of the newly generated boxplot doesn't overlap with any one of the sample boxplots, the system determines this parameter has changed.

For more information on boxplots, see Probability Boxplots.

Depending on the Application Change Sensitivity level, the system triggers model update when it observes different extent of overlapping area.

• Low—The system triggers model update only when the entire data distribution area (from the maximum value to the minimum value, that is, the entire area containing all the data) of the new boxplot doesn't have any overlapping part with that of the sample boxplots.
• Medium—The system triggers model update if the notch area (the median rectangular area in the boxplot where most of the data is located) of the new boxplot doesn't have any overlapping part with the entire data distribution areas of the sample boxplots.
• High—The system triggers model update as long as the notch area of the new boxplot doesn't have any overlapping part with that of the sample boxplots.

This option appears when you enable Dynamically update when parameters change.

The default value is 2, which means if 2 newly generated boxplots don't overlap with any one of the sample boxplots, FortiWeb automatically updates the anomaly detection model.

You can set a value from 1 to 2.

The value of the strictness level ranges from 1 to 10.

The system uses the following formula to calculate whether a sample is an anomaly:

The probability of the anomaly > μ + the strictness level * σ

If the probability of the sample is larger than the value of "μ + the strictness level * σ", this sample will be identified as anomaly.

μ and σ are calculated based on the probabilities of all the samples collected during the sample collection period, where μ is the average value of all the parameters' probabilities, σ is the standard deviation. They are fixed values. So, the value of "μ + the strictness level * σ" varies with the strictness level you set. The smaller the value of the strictness level is, the more strict the anomaly detection model will be.

This options set a global value for all the parameters. If you want to adjust the strictness level for a specific parameter, See Manage anomaly-detecting settings.

The system scans anomalies to verify whether they are attacks. It provides a method to check whether an anomaly is a real attack by the trained Support Vector Machine Model.

Click the View Threat Models link to enable or disable threat models for different types of threats such as cross-site scripting, SQL injection and code injection. Currently, seven trained Support Vector Machine Model are provided for seven attack types.

All requests are scanned first by HMM and then by Threat model.

Double click the cells in the Action Settings table to choose the action FortiWeb takes when attack is verified for each of the following situations:

• Alert—Accepts the connection and generates an alert email and/or log message.
• Alert & Deny—Blocks the request (or resets the connection) and generates an alert and/or log message.
• Period Block—Blocks the request for a certain period of time.

Enter the number of seconds that you want to block the requests. The valid range is 1–3,600 seconds (1 hour).

This option only takes effect when you choose Period Block in Action.

Select the severity level for this anomaly type. The severity level will be displayed in the alert email and/or log message.

Select a trigger policy that you have set in Log&Report > Log Policy > Trigger Policy. If potential or definite anomaly or HTTP Method Violation is detected, it will trigger the system to send email and/or log messages according to the trigger policy.