Detecting anomalous internet behavior

1. A method comprising:

performing, by a processor configured to implement a virtual security appliance:

adding or deleting one or more alert modules to or from a set of alert modules according to a predetermined schedule or as decided by a machine learning system, wherein individual ones of the alert modules in the set is configured to analyze different subsets of attribute data about connections accepted by the virtual security appliance for one or more anomalies, wherein the one or more anomalies includes:

an IP port of a particular connection,

a TCP flag of the particular connection,

a handshake of the particular connection,

a timing of the particular connection,

a duration of the particular connection, or

a termination of the particular connection;

accepting a request for a connection to the virtual security appliance;

collecting attribute data about the connection, wherein the attribute data includes date of the connection, a number of bytes associated with the connection, source IP address, or content of data sent through the connection;

filtering the collected attribute data to discard non-anomalous attribute data about the connection;

applying the set of alert modules to at least some of the attribute data about the connection to identify an incident; and

automatically generating an alert reporting the identified incident.

2. The method of claim 1 , wherein identifying the incident comprises identifying at least one anomalous connection attribute in the attribute data.

3. The method of claim 1 , further comprising supplying the alert to a user using a user interface.

4. The method of claim 1 , wherein the alert concerning the identified incident includes the time at which the incident occurred.

5. The method of claim 1 , wherein the alert module is automatically applied at fixed time intervals.

6. The method of claim 1 , further comprising formatting, using the processor, the attribute data into at least one of a plot, a table, or a chart.

7. An alert system comprising:

a memory;

at least one processor coupled to the memory, wherein the processor is configured to execute instructions stored on the memory to implement a virtual security appliance and cause the virtual security appliance to:

add or delete one or more alert modules to or from a set of alert modules according to a predetermined schedule or as decided by a machine learning system, wherein individual ones of the alert modules in the set is configured to analyze different subsets of attribute data about connections accepted by the virtual security appliance for one or more anomalies, wherein the one or more anomalies includes:

an IP port of a particular connection,

a TCP flag of the particular connection,

a handshake of the particular connection,

a timing of the particular connection,

a duration of the particular connection, or

a termination of the particular connection;

accept a request for a connection to the virtual security appliance;

collect attribute data about the connection, wherein the attribute data includes date of the connection, a number of bytes associated with the connection, source IP address, or content of data sent through the connection;

filter the collected attribute data to discard non-anomalous attribute data about the connection;

apply at least one alert module in the set to at least some of the attribute data about the connection to identify an incident; and

automatically generate an alert reporting the identified incident.

8. The system of claim 7 , wherein the virtual security appliance is further configured to identify at least one anomalous connection attribute in the attribute data.

9. The system of claim 7 , wherein the virtual security appliance is further configured to supply the alert to a user using a user interface.

10. The system of claim 7 , wherein the alert concerning the identified incident includes the time at which the incident occurred.

11. The system of claim 7 , wherein the virtual security appliance is configured to apply the alert module at fixed time intervals.

12. The system of claim 7 , wherein the virtual security appliance is further configured to format the collected attribute data into at least one of a plot, a table, or a chart.

13. A non-transitory computer-readable storage medium storing program code that when executed on a processor cause the processor to implement a virtual security appliance and to:

add or delete one or more alert modules to or from a set of alert modules according to a predetermined schedule or as decided by a machine learning system, wherein individual ones of the alert modules in the set is configured to analyze different subsets of attribute data about connections accepted by the virtual security appliance for one or more anomalies, wherein the one or more anomalies includes:

an IP port of a particular connection,

a TCP flag of the particular connection,

a handshake of the particular connection,

a timing of the particular connection,

a duration of the particular connection, or

a termination of the particular connection;

accept a request for a connection to the virtual security appliance;

collect attribute data about the connection, wherein the attribute data includes date of the connection, a number of bytes associated with the connection, source IP address, or content of data sent through the connection;

filter the collected attribute data to discard non-anomalous attribute data about the connection;

apply at least one alert module in the set to a subset of the attribute data about the connection to identify an incident; and

automatically generate an alert reporting the identified incident.

14. The non-transitory computer-readable storage medium of claim 13 , wherein the program code when executed on the processor cause the processor to supply the alert to a user using a user interface.