Security testing tool using crowd-sourced data

1. A method for security testing, the method comprising:

determining, by a computer system, information about successful payloads in payloads using crowd-sourced data in which a successful payload is a payload used in a successful attack;

determining, by the computer system, a set of popular payloads from the payloads using information about the successful payloads determined using the crowd-sourced data;

focusing, by the computer system, testing on the set of popular payloads based on a set of key features of the set of popular payloads; and

receiving, by the computer system, the crowd-sourced data from security testing tools running on a set of computing devices in a network data processing system, wherein the crowd-sourced data comprises at least one report that identifies payloads that have been tested and the results of such testing, wherein determining, by a computer system, the set of popular payloads from the payloads comprises:

ordering, by the computer system, the payloads based on a frequency that the payloads are identified as successful payloads in the crowd-sourced data; and

dividing, by the computer system, the payloads into the set of popular payloads and a set of unpopular payloads using a dynamic threshold that changes over time.

2. The method of claim 1 further comprising:

identifying, by the computer system, features for the payloads; and

identifying, by the computer system, a minimal subset of the features that distinguishes the set of popular payloads from the set of unpopular payloads to form the set of key features.

3. The method of claim 2 , wherein the features include at least one of a number of tokens, a token, an overall payload length, or a correlation between tokens, and wherein the crowd-sourced data is used to prioritize payloads that are already deployed in rules used by the computer system.

4. The method of claim 1 further comprising:

searching, by the computer system, a network data processing system for payloads that match the set of key features to identify additional popular payloads.

5. The method of claim 1 further comprising:

creating, by the computer system, new payloads having the set of key features of the set of popular payloads.

6. A security system comprising:

a computer system comprising at least one computers; and

a security testing tool running in the computer system, wherein the security testing tool determines information about successful payloads in payloads using crowd-sourced data in which a successful payload is a payload used in a successful attack; determines a set of popular payloads from the payloads using information about the successful payloads determined from the crowd-sourced data; and focuses testing on the set of popular payloads based on a set of key features for the set of popular payloads, wherein the crowd-sourced data is used to prioritize payloads that are already deployed in rules used by the security testing tool, wherein the security testing tool receives the crowd-sourced data from security testing tools running on a set of computing devices in a network data processing system, wherein the crowd-sourced data comprises at least one report that identifies payloads that have been tested and the results of such testing, wherein in determining the set of popular payloads from the payloads, the security testing tool orders the payloads based on a frequency the payloads are identified as successful payloads in the crowd-sourced data and divides the payloads into the set of popular payloads and a set of unpopular payloads using a dynamic threshold that changes over time.

7. The security system of claim 6 , wherein the security testing tool identifies features for the payloads and identifies a minimal subset of the features that distinguishes the set of popular payloads from the set of unpopular payloads to form the set of key features.

8. The security system of claim 7 , wherein the features include at least one of a number of tokens, a token, an overall payload length, or a correlation between tokens, and wherein the crowd-sourced data is used to prioritize payloads that are already deployed in rules used by the computer system.

9. The security system of claim 6 , wherein the security testing tool searches a network data processing system for payloads that match the set of key features to identify additional popular payloads.

10. The security system of claim 6 , wherein the security testing tool creates new payloads having the set of key features of the set of popular payloads.

11. A computer program product embodied in a non-transitory computer-readable storage medium for security testing comprising:

the computer-readable storage media;

first program code, stored on the computer-readable storage media, for determining information about successful payloads in payloads using crowd-sourced data in which a successful payload is a payload used in a successful attack;

second program code, stored on the computer-readable storage media, for determining a set of popular payloads from the payloads using information about the successful payloads determined using the crowd-sourced data;

third program code, stored on the computer-readable storage media, for focusing testing on the set of popular payloads based on a set of key features for the set of popular payloads; and

fourth program code, stored on the computer-readable storage media, for receiving the crowd-sourced data from security testing tools running on a set of computing devices in a network data processing system, wherein the crowd-sourced data comprises at least one report that identifies payloads that have been tested and the results of such testing, wherein the second program code comprises:

program code, stored on the computer-readable storage media, for ordering the payloads based on a frequency that the payloads are identified as successful payloads in the crowd-sourced data; and

program code, stored on the computer-readable storage media, for dividing the payloads into the set of popular payloads and a set of unpopular payloads using a dynamic threshold that changes over time.

12. The computer program product of claim 11 further comprising:

fourth program code, stored on the computer-readable storage media, for identifying features for the payloads; and

fifth program code, stored on the computer-readable storage media, for identifying a minimal subset of the features that distinguishes the set of popular payloads from the set of unpopular payloads to form the set of key features.

13. The computer program product of claim 11 further comprising:

fourth program code, stored on the computer-readable storage media, for searching a network data processing system for payloads that match the set of key features to identify additional popular payloads.

14. The computer program product of claim 11 further comprising:

fourth program code, stored on the computer-readable storage media, for creating new payloads having the set of key features of the set of popular payloads.