METHODS, SYSTEMS, AND MEDIA FOR DETECTING COVERT MALWARE

• US 20130333037A1
• Filed: 08/13/2013
• Published: 12/12/2013
• Est. Priority Date: 12/31/2009
• Status: Active Grant

• Current Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Original Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Inventors: Bowen, Brian M., Prabhu, Pratap V., Kemerlis, Vasileios P., Sidiroglou, Stylianos, Stolfo, Salvatore J., Keromytis, Angelos D.
• Granted Patent: US 9,971,891 B2
• Time in Patent Office: Days
• US Class Current: 726/23
• CPC Class Codes: G06F 21/56 Computer malware detection or handling, e.g. anti-virus arrangements
• CPC Class Codes: G06F 21/566 Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: H04L 63/1441 Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/1491 using deception as countermeasure, e.g. honeypots, honeynets, decoys or entrapment

Abstract

Methods, systems, and media for detecting covert malware are provided. In accordance with some embodiments, a method for detecting covert malware in a computing environment is provided, the method comprising: receiving a first set of user actions; generating a second set of user actions based on the first set of user actions and a model of user activity; conveying the second set of user actions to an application inside the computing environment; determining whether state information of the application matches an expected state after the second set of user actions is conveyed to the application; and determining whether covert malware is present in the computing environment based at least in part on the determination.

Claims

1. A method for detecting covert malware in a computing environment, the method comprising:

receiving a first set of user actions;

generating a second set of user actions based on the first set of user actions and a model of user activity;

conveying the second set of user actions to an application inside the computing environment;

determining whether state information of the application matches an expected state after the second set of user actions is conveyed to the application; and

determining whether covert malware is present in the computing environment based at least in part on the determination.

2. The method of claim 1, wherein the second set of user actions is generated outside of the computing environment.

3. The method of claim 1, further comprises:

determining whether a decoy corresponding to the second set of user actions has been accessed by an unauthorized entity; and

in response to determining that the decoy has been accessed by the unauthorized entity, determining that covert malware is present in the computing environment.

4. The method of claim 1, wherein the first set of user actions comprises mouse and keyboard events.

5. The method of claim 4, further comprising replaying at least a portion of the first set of user actions along with conveying the second set of user actions.

6. The method of claim 4, wherein generating the second set of user actions further comprises recording, modifying, and replaying the mouse and keyboard events based on the first set of user actions.

7. The method of claim 1, wherein the second set of user actions is generated by modifying the first set of user actions and translating the first set of user actions using a wireless protocol.

8. The method of claim 1, further comprising defining the second set of user actions by a formal language, wherein the first set of user actions is mapped to constructs of the formal language and wherein the formal language comprises carry actions for the simulation and the conveyance of the decoy and cover actions that support believability of the second set of user actions and the decoy.

9. The method of claim 1, wherein the model of user activity includes a model of at least one of:

keystroke speed, mouse speed, mouse distance, keystroke error rate, and frequency of errors made during typing.

10. The method of claim 1, wherein determining the state information further comprises performing a visual verification that determines whether a screen output changed as expected in response to the second set of user actions.

11. The method of claim 1, wherein determining the state information further comprises:

analyzing network traffic to determine message characteristics that include at least one of;

a number of conversations, a number of messages exchanged, and a number of bytes in each message; and

comparing the state information that includes current message characteristics with the analyzed network traffic that includes determined message characteristics.

12. A system for detecting covert malware in a computing environment, the system comprising:

a hardware processor that is configured to;

receive a first set of user actions;

generate a second set of user actions based on the first set of user actions and a model of user activity;

convey the second set of user actions to an application inside the computing environment;

determine whether state information of the application matches an expected state after the second set of user actions is conveyed to the application; and

determine whether covert malware is present in the computing environment based at least in part on the determination.

13. The system of claim 12, wherein the second set of user actions is generated outside of the computing environment.

14. The system of claim 12, wherein the hardware processor is further configured to:

determine whether a decoy corresponding to the second set of user actions has been accessed by an unauthorized entity, and

in response to determining that the decoy has been accessed by the unauthorized entity, determine that covert malware is present in the computing environment.

15. The system of claim 12, wherein the first set of user actions comprises mouse and keyboard events.

16. The system of claim 15, wherein the hardware processor is further configured to replay at least a portion of the first set of user actions along with conveying the second set of user actions.

17. The system of claim 15, wherein the hardware processor is further configured to record, modify, and replay the mouse and keyboard events based on the first set of user actions.

18. The system of claim 12, wherein the second set of user actions is generated by modifying the first set of user actions and translating the first set of user actions using a wireless protocol.

19. The system of claim 12, wherein the hardware processor is further configured to define the second set of user actions by a formal language, wherein the first set of user actions is mapped to constructs of the formal language and wherein the formal language comprises carry actions for the simulation and the conveyance of the decoy and cover actions that support believability of the second set of user actions and the decoy.

20. The system of claim 12, wherein the model of user activity includes a model of at least one of:

keystroke speed, mouse speed, mouse distance, keystroke error rate, and frequency of errors made during typing.

21. The system of claim 12, wherein the hardware processor is further configured to perform a visual verification that determines whether a screen output changed as expected in response to the second set of user actions.

22. The system of claim 13, wherein the hardware processor is further configured to:

analyze network traffic to determine message characteristics that include at least one of;

a number of conversations, a number of messages exchanged, and a number of bytes in each message; and

compare the state information that includes current message characteristics with the analyzed network traffic that includes determined message characteristics.

23. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for detecting covert malware in a computing environment, the method comprising:

receiving a first set of user actions;

generating a second set of user actions based on the first set of user actions and a model of user activity;

conveying the second set of user actions to an application inside the computing environment;

determining whether state information of the application matches an expected state after the second set of user actions is conveyed to the application; and

determining whether covert malware is present in the computing environment based at least in part on the determination.