System and method for detection of malicious hypertext transfer protocol chains

1. A system configured to detect malware comprising:

a device on a network to:

intercept one or more communication packets in transit between a first digital device and a second digital device on the network;

analyze a payload of at least one of the one or more communication packets;

detect, based on analyzing the payload, one or more hypertext transfer objects, in a chain of a plurality of hypertext transfer objects, in the payload in transit between the first digital device and the second digital device on the network;

analyze the one or more hypertext transfer objects for one or more events;

generate a list of events based on analyzing the one or more hypertext transfer objects;

determine a score based on at least one of a file format or a header within the one or more hypertext transfer objects;

determine that the one or more hypertext transfer objects is a suspicious sequence of hypertext transfer objects based on the score satisfying a threshold; and

instantiate a browser cooking environment based on determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects.

2. The system of claim 1 , wherein the one or more hypertext transfer objects include a body.

3. The system of claim 2 , wherein the device, when analyzing the one or more hypertext transfer objects, is to:

analyze the one or more hypertext transfer objects by performing a frequency analysis of the body.

4. The system of claim 3 , wherein the frequency analysis includes determining a correlation between at least one of:

a group of special characters,

a number of language blocks, or

an entropy calculation.

5. The system of claim 1 , wherein the device, when analyzing the one or more hypertext transfer objects, is to:

analyze the one or more hypertext transfer objects by correlating a unique resource identifier with a body file type.

6. The system of claim 1 , wherein the device is further to:

determine whether the one or more hypertext transfer objects include one or more calls to at least one of a web counter and one or more web analytics engines; and

wherein the device, when determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects, is to:

determine that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining that the one or more hypertext transfer objects include the one or more calls.

7. The system of claim 1 , wherein the device is further to:

determine one or more vulnerabilities of an application based on a user-agent field in the header; and

wherein the device, when determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects, is to:

determine that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining the one or more vulnerabilities.

8. The system of claim 1 , wherein the device is further to:

determine a reputation of a domain name in the one or more hypertext transfer objects, and

wherein the device, when determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects, is to:

determine that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining the reputation.

9. The system of claim 1 , wherein the device is further to:

determine network information of the one or more hypertext transfer objects,

the network information including at least one of an Internet protocol address or a port number; and

wherein the device, when determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects, is to:

determine that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining the network information.

10. The system of claim 1 , wherein the device is further to:

generate a data model based on the list of events.

11. The system of claim 1 , wherein the device is further to:

classify the chain of the plurality of hypertext transfer objects based on the list of events.

12. The system of claim 1 , wherein the device is further to:

generate a data model based on the list of events; and

classify the of the plurality of hypertext transfer objects chain based on the data model.

13. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause one or more processors to:

intercept one or more communication packets in transit between a first digital device and a second digital device on a network;

analyze a payload of at least one of the one or more communication packets;

detect, based on analyzing the payload, one or more hypertext transfer objects, in a chain of a plurality of hypertext transfer objects, in the payload in transit between the first digital device and the second digital device on the network;

analyze the one or more hypertext transfer objects for one or more events;

generate a list of events based on analyzing the one or more hypertext transfer objects;

determine a score based on at least one of a file format or a header within the one or more hypertext transfer objects; and

determine that the one or more hypertext transfer objects is a suspicious sequence of hypertext transfer objects based on the score satisfying a threshold.

14. A method, comprising:

intercepting, by a device, one or more communication packets in transit between a first digital device and a second digital device on a network;

analyzing, by the device, a payload of at least one of the one or more communication packets;

detecting, by the device and based on analyzing the payload, one or more hypertext transfer objects, in a chain of a plurality of hypertext transfer objects, in the payload in transit between the first digital device and the second digital device on the network;

analyzing, by the device, the one or more hypertext transfer objects for one or more events;

generating, by the device, a list of events based on analyzing the one or more hypertext transfer objects;

determining, by the device, a score based on at least one of a file format or a header within the one or more hypertext transfer objects; and

determining, by the device, that the one or more hypertext transfer objects is a suspicious sequence of hypertext transfer objects based on the score satisfying a threshold.

15. The method of claim 14 , wherein the one or more hypertext transfer objects includes a body.

16. The method of claim 15 , wherein analyzing the one or more hypertext transfer objects includes:

analyzing the one or more hypertext transfer objects by performing a frequency analysis of the body.

17. The method of claim 16 , wherein performing the frequency analysis includes:

determining a correlation between at least one of:

a group of special characters,

a number of language blocks, or

an entropy calculation.

18. The method of claim 14 , wherein analyzing the one or more hypertext transfer objects includes:

correlating a unique resource identifier with a body file type.

19. The method of claim 14 , further comprising:

determining whether the one or more hypertext transfer objects include one or more references to at least one of a web counter and one or more web analytics engines; and

wherein determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects comprises:

determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining that the one or more hypertext transfer objects include the one or more references.

20. The method of claim 14 , further comprising:

determining one or more vulnerabilities of an application based on a user-agent field in the header; and

wherein determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects comprises:

determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining the one or more vulnerabilities.

21. The method of claim 14 , further comprising:

determining a reputation of a domain name in the one or more hypertext transfer objects; and

wherein determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects comprises:

determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining the reputation.

22. The method of claim 14 , further comprising,

determining network information of the one or more hypertext transfer objects,

the network information including at least one of an Internet protocol address or a port number; and

wherein determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects comprises:

determining that the one or more hypertext transfer objects is the suspicious sequence of hypertext transfer objects based on determining the network information.

23. The method of claim 14 , further comprising:

generating a data model based on the list of events.

24. The method of claim 14 , further comprising:

classifying the chain of the plurality of hypertext transfer objects based on the list of events.

25. The method of claim 14 , further comprising:

generating a data model based on the list of events; and

classifying the chain based on the data model.