Passive decryption of encrypted traffic to generate more accurate machine learning training data

1. A method comprising:

capturing a memory dump of a device in a sandbox environment executing a malware sample;

identifying a cryptographic key based on a particular data structure in the captured memory dump;

using the identified cryptographic key to decrypt encrypted traffic sent by the device;

labeling at least a portion of the decrypted traffic sent by the device as benign; and

training a machine learning-based traffic classifier to classify encrypted traffic in a network as malicious or non-malicious based on the at least a portion of the decrypted traffic sent by the device and labeled as benign.

2. The method as in claim 1 , further comprising:

deploying the machine learning-based traffic classifier to a node in the network, to detect the presence of malware in the network.

3. The method as in claim 1 , wherein the particular data structure comprises a wrapper for the cryptographic key, the method further comprising:

identifying a particular encryption suite used by the device to encrypt the traffic; and

identifying the data structure based on the identified encryption suite used by the device to encrypt the traffic.

4. The method as in claim 1 , wherein capturing the memory dump of the device executing the malware sample comprises:

detecting a triggering condition to initiate the memory dump.

5. The method as in claim 4 , wherein the triggering condition to initiate the memory dump comprises one of: a Change Cipher Spec message appearing in the traffic of the device, multiple socket.send( ) calls to a particular 5-tuple being observed, detecting multiple calls to a particular application programming interface (API) of the device.

6. The method as in claim 1 , wherein identifying the encryption key based on the particular data structure in the captured memory dump comprises:

identifying a set of bytes in the memory dump having high entropy in comparison to bytes preceding or following the set of bytes in the memory dump.

7. The method as in claim 1 , wherein the traffic sent by the device is encrypted using Transport Layer Security (TLS).

8. An apparatus, comprising:

one or more network interfaces to communicate with a network;

a processor coupled to the network interfaces and configured to execute one or more processes; and

a memory configured to store a process executable by the processor, the process when executed configured to:

capture a memory dump of a device in a sandbox environment executing a malware sample;

identify a cryptographic key based on a particular data structure in the captured memory dump;

use the identified cryptographic key to decrypt encrypted traffic sent by the device;

label at least a portion of the decrypted traffic sent by the device as benign; and

train a machine learning-based traffic classifier to classify encrypted traffic in the network as malicious or non-malicious based on the at least a portion of the decrypted traffic sent by the device and labeled as benign.

9. The apparatus as in claim 8 , wherein the process when executed is further configured to:

deploy the machine learning-based traffic classifier to a node in the network, to detect the presence of malware in the network.

10. The apparatus as in claim 8 , wherein the particular data structure comprises a wrapper for the cryptographic key, wherein the process when executed is further configured to:

identify a particular encryption suite used by the device to encrypt the traffic; and

identify the data structure based on the identified encryption suite used by the device to encrypt the traffic.

11. The apparatus as in claim 8 , wherein the apparatus captures the memory dump of the device executing the malware sample by:

detecting a triggering condition to initiate the memory dump.

12. The apparatus as in claim 11 , wherein the triggering condition to initiate the memory dump comprises one of: a Change Cipher Spec message appearing in the traffic of the device, multiple socket.send( ) calls to a particular 5-tuple being observed, detecting multiple calls to a particular application programming interface (API) of the device.

13. The apparatus as in claim 8 , wherein the apparatus identifies the encryption key based on the particular data structure in the captured memory dump by:

identifying a set of bytes in the memory dump having high entropy in comparison to bytes preceding or following the set of bytes in the memory dump.

14. The apparatus as in claim 8 , wherein the traffic sent by the device is encrypted using Transport Layer Security (TLS).

15. A tangible, non-transitory, computer-readable medium storing program instructions that cause a computing device to execute a process comprising:

capturing a memory dump of a device in a sandbox environment executing a malware sample;

identifying a cryptographic key based on a particular data structure in the captured memory dump;

using the identified cryptographic key to decrypt encrypted traffic sent by the device;

labeling at least a portion of the decrypted traffic sent by the device in the sandbox environment as benign; and

training a machine learning-based traffic classifier to classify encrypted traffic in a network as malicious or non-malicious based on the at least a portion of the decrypted traffic sent by the device in the sandbox environment and labeled as benign.

16. The computer-readable medium as in claim 15 , wherein capturing the memory dump of the device executing the malware sample comprises:

detecting a triggering condition to initiate the memory dump.

17. The computer-readable medium as in claim 16 , wherein the triggering condition to initiate the memory dump comprises one of: a Change Cipher Spec message appearing in the traffic of the device, multiple socket.send( ) calls to a particular 5-tuple being observed, detecting multiple calls to a particular application programming interface (API) of the device.

18. The computer-readable medium as in claim 16 , wherein the traffic sent by the device is encrypted using Transport Layer Security (TLS).

19. The computer-readable medium as in claim 16 , wherein the program instructions when executed is further configured to:

deploy the machine learning-based traffic classifier to a node in the network, to detect the presence of malware in the network.

20. The computer-readable medium as in claim 15 , wherein the program instructions identify the encryption key based on the particular data structure in the captured memory dump by:

identifying a set of bytes in the memory dump having high entropy in comparison to bytes preceding or following the set of bytes in the memory dump.