Extracting encryption metadata and terminating malicious connections using machine learning

1. A network traffic hub for detecting malicious behavior based on network communications establishing an encrypted connection between a smart appliance and a remote server, the network traffic hub comprising:

a network interface communicatively coupled to a smart appliance via a first communication channel in a local network and communicatively coupled to a remote server via a second communication channel in a wide area network;

a processor; and

a memory storing program code, the program code when executed causes the processor to:

intercept, via the first communication channel, a first network communication from the smart appliance for transmission, via the second communication channel, to the remote server, the first network communication comprising an encryption suite that comprises one or more encryption algorithms the smart appliance uses to encrypt network traffic with the remote server;

extract the encryption suite from the first network communication;

transmit, via the second communication channel, the first network communication to the remote server;

intercept, via the second communication channel, one or more second network communications from the remote server to the smart appliance, the one or more second network communications comprising a public certificate associated with the remote server and an identified subset of the one or more encryption algorithms;

extract the public certificate and the identified subset of the one or more encryption algorithms from the one or more second network communications;

transmit, via the first communication channel, the one or more second network communications to the smart appliance;

detect malicious behavior performed by the smart appliance or remote server, via the first and second communication channels, based on the encryption suite, public certificate, and the identified subset of the one or more encryption algorithms; and

block network communications between the smart appliance and the remote server in response to detecting malicious behavior in the network communications between the smart appliance and the remote server.

2. The network traffic hub of claim 1 , further comprising program code to allow, responsive to a lack of detection of malicious behavior in the network communications between the smart appliance and the remote server, continued network traffic communications between the smart appliance and the remote server.

3. The network traffic hub of claim 1 , wherein the program code to detect malicious behavior in encrypted network communications further comprises program code to detect using a threat detection model.

4. The network traffic hub of claim 3 , wherein the threat detection model is a decision tree.

5. The network traffic hub of claim 3 , wherein the threat detection model is a machine-learned model.

6. The network traffic hub of claim 5 , wherein the threat detection model is trained using known-malicious training data and known-benign training data, the known-malicious training data comprising examples where a secure connection between a smart appliance and a remote server is attacked while the secure connection was being established and the known-benign training data comprising examples where a secure connection between a smart appliance and a remote server is successful connected without being compromised.

7. The network traffic hub of claim 1 , further comprising program code to receive a designation, from a behavior analysis engine, of whether malicious behavior is present in the first network communication or the one or more second network communications based on analysis from a threat detection model.

8. A method for detecting malicious behavior based on network communications establishing an encrypted connection between a smart appliance and a remote server, comprising:

intercepting, by a network traffic hub in a local network, a first network communication from a smart appliance in the local network, for transmission to a remote server in a wide area network, the first network communication comprising an encryption suite that comprises one or more encryption algorithms the smart appliance uses to encrypt network traffic with the remote server;

extracting the encryption suite from the first network communication;

transmitting, by the network traffic hub, the first network communication to the remote server;

intercepting, by the network traffic hub, one or more second network communications from the remote server to the smart appliance, the one or more second network communications comprising a public certificate associated with the remote server and an identified subset of the one or more encryption algorithms;

extracting the public certificate and the identified subset of the one or more encryption algorithms from the one or more second network communications;

transmitting, by the network traffic hub, the one or more second network communications to the smart appliance;

detecting malicious behavior performed by the smart appliance or remote server, based on the encryption suite, public certificate, and the identified subset of the one or more encryption algorithms; and

blocking network communications between the smart appliance and the remote server, responsive to detecting malicious behavior in the network communications between the smart appliance and the remote server by the network traffic hub.

9. The method of claim 8 , further comprising, responsive to not detecting malicious behavior in the network communications between the smart appliance and the remote server, allowing continued network traffic communications between the smart appliance and the remote server by the network traffic hub.

10. The method of claim 8 , wherein detecting malicious behavior in encrypted network communications uses a threat detection model.

11. The method of claim 10 , wherein the threat detection model is a decision tree.

12. The method of claim 10 , wherein the threat detection model is a machine-learned model.

13. The method of claim 12 , wherein the threat detection model is trained using known-malicious training data and known-benign training data, the known-malicious training data comprising examples where a secure connection between a smart appliance and a remote server is attacked while the secure connection was being established and the known-benign training data comprising examples where a secure connection between a smart appliance and a remote server is successful connected without being compromised.

14. The method of claim 8 , further comprising receiving, from a behavior analysis engine a designation of whether malicious behavior is present in the first network communication or the one or more second network communications based on analysis from a threat detection model.

15. A non-transitory computer-readable medium comprising stored program code, the program code comprised of computer-executable instructions that, when executed by a processor, causes the processor to:

intercept, via a first communication channel in a local network, a first network communication from a smart appliance for transmission, via a second communication channel in a wide area network, to a remote server, the first network communication comprising an encryption suite that comprises one or more encryption algorithms the smart appliance uses to encrypt network traffic with the remote server;

extract the encryption suite from the first network communication;

transmit, via the second communication channel, the first network communication to the remote server;

intercept, via the second communication channel, one or more second network communications from the remote server to the smart appliance, the one or more second network communications comprising a public certificate associated with the remote server and an identified subset of the one or more encryption algorithms;

extract the public certificate and the identified subset of the one or more encryption algorithms from the one or more second network communications;

transmit, via the first communication channel, the one or more second network communications to the smart appliance;

detect malicious behavior performed by the smart appliance or remote server, via the first and second communication channels, based on the encryption suite, public certificate, and the identified subset of the one or more encryption algorithms; and

block network communications between the smart appliance and the remote server, in response to detecting malicious behavior in the network communications between the smart appliance and the remote server.

16. The non-transitory computer-readable medium of claim 15 , further comprising, responsive to not detecting malicious behavior in the network communications between the smart appliance and the remote server, allowing continued network traffic communications between the smart appliance and the remote server.

17. The non-transitory computer-readable medium of claim 15 , wherein the program code further comprises instruction to detect malicious behavior in encrypted network communications using a threat detection model.

18. The non-transitory computer-readable medium of claim 17 , wherein the threat detection model is a decision tree.

19. The non-transitory computer-readable medium of claim 17 , wherein the threat detection model is a machine-learned model.

20. The non-transitory computer-readable medium of claim 19 , wherein the threat detection model is trained using known-malicious training data and known-benign training data, the known-malicious training data comprising examples where a secure connection between a smart appliance and a remote server is attacked while the secure connection was being established and the known-benign training data comprising examples where a secure connection between a smart appliance and a remote server is successful connected without being compromised.

21. The non-transitory computer-readable medium of claim 15 , further comprising program code to receive a designation, from a behavior analysis engine, of whether malicious behavior is present in the first network communication or the one or more second network communications based on analysis from a threat detection model.