Systems and methods for application-level security
A system and method for application-level security is disclosed. Application-level security refers to detecting security threats from functional user behavior. Information about functional user behavior is transformed into a behavior session. The behavior session is transformed into a behavior pattern. The behavior pattern is correlated with evaluation patterns to detect potential security threats. Behavior patterns are collected over time to improve analysis.
1. A method for detecting malicious functional user behavior on a web application comprising:
collecting a plurality of user requests sent from a client device to a web server hosting the web application during a session, wherein:
the plurality of user requests comprise information associated with functional user behavior; and
the web application is provided by the web server to the client device over the Internet;
creating a behavior pattern comprising a first mapping from a plurality of qualitative attributes to respective user values in the functional user behavior, wherein the user values are derived from parsing the information associated with the functional user behavior according to the qualitative attributes;
correlating the created behavior pattern with each evaluation pattern of a set of known evaluation patterns, wherein at least one of the known evaluation patterns is associated with malicious functional user behavior and comprises a second mapping from at least one of the plurality of qualitative attributes in the created behavior pattern to one or more respective evaluation values; and
in response to finding a match between the behavior pattern and the evaluation pattern associated with the malicious functional user behavior from the correlation, automatically executing a remediation action associated with the matching evaluation pattern.
2. The method defined in claim 1 wherein the information associated with the functional user behavior includes at least one of: qualitative information about user activity on the web application and quantitative information about user activity on the web application.
3. The method defined in claim 1 wherein the behavior pattern comprises quantifiable information about certain qualitative functional user behavior characteristics, the qualitative functional user behavior characteristics including at least one of: user input on a web page, a web page browsing sequence, timestamp of user input, and timestamp of page visits in the browsing sequence.
4. The method defined in claim 1 wherein the user input comprises at least one of: text input, mouse clicks, cursor movements, and haptic input.
5. The method defined in claim 1 wherein the session is a user behavior session that begins at a first time when interaction with the web application begins to a second time when the interaction with the web application stops.
6. The method defined in claim 1 further comprising storing a plurality of behavior patterns over time in a data storage device.
7. The method defined in claim 1 wherein one of the evaluation values comprises a specific regular expression.
8. The method defined in claim 1 wherein one of the evaluation values comprises a particular sequence of web pages.
9. The method defined in claim 1 wherein the evaluation patterns are specific to the web application and are generated from a set of non-application specific behavior patterns.
10. The method defined in claim 1 wherein the evaluation patterns are hierarchically related.
11. The method defined in claim 1 wherein correlating comprises classifying the behavior pattern using statistical correlation algorithms and heuristics, wherein statistical correlation algorithms comprise quantitatively calculating the similarity between the behavior pattern and each of the evaluation patterns.
12. The method defined in claim 1 further comprising updating the evaluation patterns based at least in part on heuristics.
13. The method defined in claim 6 further comprising clustering behavior patterns collected against the evaluation patterns to extract one or more new evaluation patterns.
14. The method defined in claim 6 further comprising updating the evaluation patterns based at least in part on behavior patterns collected.
15. The method defined in claim 11 wherein classifying the behavior pattern is based at least in part on the presence or absence of certain functional characteristics in the behavior pattern.
16. The method defined in claim 1 wherein the remediation action comprises sending a notification to a system administrator.
17. The method defined in claim 1 wherein the remediation action comprises blocking the user from accessing the web application for a certain period of time.
18. The method defined in claim 1 wherein the remediation action comprises modifying the session by displaying an alerting on the web application.
19. The method defined in claim 1 further comprising collecting a plurality of user requests across a plurality of distributed networks and access points.
20. A system for detecting malicious functional user behavior on a web application, comprising:
control circuitry on a server configured to receive a plurality of user requests sent from a client device to a web server hosting the web application during a session, wherein:
the plurality of user requests comprise information associated with functional user behavior; and
the web application is provided by the web server to the client device over the Internet;
create a behavior pattern comprising a first mapping from a plurality of qualitative attributes to respective user values in the functional user behavior, wherein the user values are derived from parsing the information in the received user requests according to the qualitative attributes;
a data storage in the server for storing the plurality of user requests and the behavior pattern; and
a processor on the server for:
correlating the created behavior pattern with each evaluation pattern of a set of known evaluation patterns, wherein at least one of the known evaluation patterns is associated with malicious functional user behavior and comprises a second mapping from at least one of the plurality of qualitative attributes in the created behavior pattern to one or more respective evaluation values;
in response to finding a match between the behavior pattern and the evaluation pattern associated with the malicious functional user behavior, automatically executing a remediation action associated with the matching evaluation pattern.
21. The system defined in claim 20 wherein the information associated with the functional user behavior includes at least one of: qualitative information about user activity on the web application and quantitative information about user activity on the web application.
22. The system defined in claim 20 wherein the behavior pattern comprises quantifiable information about certain qualitative functional user behavior characteristics, the qualitative functional user behavior characteristics including at least one of: user input on a web page, a web page browsing sequence, timestamp of user input, and timestamp of page visits in the browsing sequence.
23. The system defined in claim 20 wherein the user input comprises at least one of: text input, mouse clicks, cursor movements, and haptic input.
24. The system defined in claim 20 wherein the session is a user behavior session that begins at a first time when interaction with the web application begins to a second time when the interaction with the web application stops.
25. The system defined in claim 20 wherein the data storage is configured for storing a plurality of behavior patterns over time.
26. The system defined in claim 20 wherein one of the evaluation values comprises a specific regular expression.
27. The system defined in claim 20 wherein one of the evaluation values comprises a particular sequence of web pages.
28. The system defined in claim 20 wherein the evaluation patterns are specific to the web application and are generated from a set of non-application specific behavior patterns.
29. The system defined in claim 20 wherein the evaluation patterns are hierarchically related.
30. The system defined in claim 20 wherein correlating comprises classifying the behavior pattern using statistical correlation algorithms and heuristics, wherein statistical correlation algorithms comprise quantitatively calculating the similarity between the behavior pattern and each of the evaluation patterns.
31. The system defined in claim 20 wherein the processor is further configured to update the evaluation patterns based at least in part on heuristics.
32. The system defined in claim 25 wherein the processor is further configured to cluster behavior patterns collected against the evaluation patterns to extract one or more new evaluation patterns.
33. The system defined in claim 25 wherein the processor is further configured to update the evaluation patterns based at least in part on behavior patterns collected.
34. The system defined in claim 30 wherein classifying the behavior pattern is based at least in part on the presence or absence of certain functional characteristics in the behavior pattern.
35. The system defined in claim 20 wherein the remediation action comprises sending a notification to a system administrator.
36. The system defined in claim 20 wherein the remediation action comprises blocking the user from accessing the web application for a certain period of time.
37. The system defined in claim 20 wherein the remediation action comprises modifying the session by displaying an alerting on the web application.
38. The system defined in claim 20 wherein the control circuitry is further configured to receive a plurality of user requests across a plurality of distributed networks and access points.