Dynamic variance mechanism for securing enterprise resources using a virtual private network
Disclosed are various examples for securing enterprise resources using a virtual private network. At least one computing device that can authenticate a client device for a virtual private network (VPN) connection based on a first device identifier received from the client device and a second device identifier received from a remote management service. The at least one computing device can determine that a network event associated with the client device has been observed and execute a machine learning routine to identify a pattern of access for the client device. A network access anomaly is determined in response to a network interaction of the client device deviating from the pattern of access for the client device. A remedial action is performed based on an anomaly type associated with the network access anomaly.
1. A system, comprising:
at least one computing device; and
program instructions stored in memory of the at least one computing device and executable by the at least one computing device that, when executed, direct the at least one computing device to at least:
authenticate a client device for using a virtual private network (VPN) for network communications, wherein the authentication is based on a first device identifier (ID) received from the client device and on a second device ID generated by a management service executing on the at least one computing device;
train a machine learning (ML) routine to identify a pattern from past network communications of the client device;
in response to observing a new network communication of the client device, determine, using the trained ML routine, that the new network communication is an anomaly;
in response to determining that the new network communication is an anomaly: pause the VPN from being used by the client device for the network communications, and instruct the client device to reauthenticate with the at least one computing device to resume using the VPN, wherein pausing the VPN includes sending a control message to pause network traffic, to a VPN application residing on the client device; and
in response to the client device reauthenticating with the at least one computing device: resume the network communications of the client device using the VPN, and further train the ML routine using the anomaly.
2. The system of claim 1 , wherein the ML routine is trained by inputting into the ML routine, historical data describing the past network communications of the client device.
3. The system of claim 1 , wherein authenticating the client device for using the VPN includes: receiving the first device ID and the second device ID from the client device, transmitting the first device ID to the management service, and receiving the second device ID from the management service.
4. The system of claim 1 , wherein pausing the VPN and instructing the client device to reauthenticate, include transmitting the control message to the VPN application executing on the client device, the control message directing the client device to pause a flow of network traffic and to reauthenticate.
5. The system of claim 1 , wherein resuming the network communications of the client device using the VPN includes notifying the VPN application executing on the client device, that the client device has been verified based on a hyperlink transmitted to the client device.
6. A computer-implemented method, comprising:
authenticating a client device for using a virtual private network (VPN) for network communications, wherein the authentication is based on a first device identifier (ID) received from the client device and on a second device ID generated by a management service executing on at least one computing device;
training a machine learning (ML) routine to identify a pattern from past network communications of the client device;
in response to observing a new network communication of the client device, determining, using the trained ML routine, that the new network communication is an anomaly;
in response to determining that the new network communication is an anomaly: pausing the VPN from being used by the client device for the network communications, and instructing the client device to reauthenticate with the at least one computing device to resume using the VPN, wherein pausing the VPN includes sending a control message to pause network traffic, to a VPN application residing on the client device; and
in response to the client device reauthenticating with the at least one computing device: resuming the network communications of the client device using the VPN, and further training the ML routine using the anomaly.
7. The computer-implemented method of claim 6 , wherein the ML routine is trained by inputting into the ML routine, historical data describing the past network communications of the client device.
8. The computer-implemented method of claim 6 , wherein authenticating the client device for using the VPN includes: receiving the first device ID and the second device ID from the client device, transmitting the first device ID to the management service, and receiving the second device ID from the management service.
9. The computer-implemented method of claim 6 , wherein pausing the VPN and instructing the client device to reauthenticate, include transmitting the control message to the VPN application executing on the client device, the control message directing the client device to pause a flow of network traffic and to reauthenticate.
10. The computer-implemented method of claim 6 , wherein resuming the network communications of the client device using the VPN includes notifying the VPN application executing on the client device, that the client device has been verified based on a hyperlink transmitted to the client device.
11. A non-transitory computer-readable medium comprising program instructions stored thereon executable in a computing device that, when executed, direct the computing device to at least:
authenticate a client device for using a virtual private network (VPN) for network communications, wherein the authentication is based on a first device identifier (ID) received from the client device and on a second device ID generated by a management service executing on the computing device;
train a machine learning (ML) routine to identify a pattern from past network communications of the client device;
in response to observing a new network communication of the client device, determine, using the trained ML routine, that the new network communication is an anomaly;
in response to determining that the new network communication is an anomaly: pause the VPN from being used by the client device for the network communications, and instruct the client device to reauthenticate with the computing device to resume using the VPN, wherein pausing the VPN includes sending a control message to pause network traffic, to a VPN application residing on the client device; and
in response to the client device reauthenticating with the computing device: resume the network communications of the client device using the VPN, and further train the ML routine using the anomaly.
12. The non-transitory computer-readable medium of claim 11 , wherein the ML routine is trained by inputting into the ML routine, historical data describing the past network communications of the client device.
13. The non-transitory computer-readable medium of claim 11 , wherein authenticating the client device for using the VPN includes: receiving the first device ID and the second device ID from the client device, transmitting the first device ID to the management service, and receiving the second device ID from the management service.
14. The non-transitory computer-readable medium of claim 11 , wherein pausing the VPN and instructing the client device to reauthenticate, include transmitting the control message to the VPN application executing on the client device, the control message directing the client device to pause a flow of network traffic and to reauthenticate.
15. The non-transitory computer-readable medium of claim 11 , wherein resuming the network communications of the client device using the VPN includes notifying the VPN application executing on the client device, that the client device has been verified based on a hyperlink transmitted to the client device.
16. The system of claim 3 , wherein authenticating the client device for using the VPN further includes determining that the second device ID received from the client device matches the second device ID received from the management service.
17. The system of claim 1 , wherein the program instructions, when executed, further direct the at least one computing device to at least:
upon authenticating the client device for using the VPN, encrypt data and transmit the encrypted data to the client device using the VPN.
18. The computer-implemented method of claim 8 , wherein authenticating the client device for using the VPN further includes determining that the second device ID received from the client device matches the second device ID received from the management service.
19. The computer-implemented method of claim 6 , further comprising:
upon authenticating the client device for using the VPN, encrypting data and transmitting the encrypted data to the client device using the VPN.
20. The non-transitory computer-readable medium of claim 13 , wherein authenticating the client device for using the VPN further includes determining that the second device ID received from the client device matches the second device ID received from the management service.