Systems and methods for traffic inspection via an embedded browser
View Patent ↗Described embodiments provide systems and methods for traffic inspection via embedded browsers. An application inspector module of an embedded browser executable on a client may intercept network traffic for an application. The network traffic may include packets exchanged between the application and the server via a channel. The application inspector module may identify a computing resource usage on the client in providing a user with access to the application via the embedded browser. The application inspector module may generate analytics data based on the intercepted network traffic and the computing resource usage. The application inspector module may maintain a user behavior profile based on the analytics data. The application inspector module may determine that a portion of the network traffic directed to the remote server contains sensitive information. Responsive to the determination, the application inspector module may block or remove the portion of the network traffic.
1. A method comprising:
monitoring, by a client device, traffic of an application hosted on one or more remote computing devices and accessed via the client device;
providing, by the client device responsive to monitoring, data associated with traffic of the application as input to a model, the model configured to output identification of a predicted behavior of a user responsive to the input;
causing, by the device, access to the application by the user to be restricted responsive to the identification of the predicted behavior of the user from using the model; and
using, by the client device, one or more weights with the model, the one or more weights determined based at least on the data.
2. The method of claim 1 , further comprising accessing, by the client device, the application via browser within a client application of the client device.
3. The method of claim 1 , further comprising generating, by the client device from monitoring, the data identifying one or more interactions of the user with the application.
4. The method of claim 1 , further comprising determining, by the client device, a deviation from the predicted behavior and a behavior of the user measured from monitoring.
5. The method of claim 1 , further comprising determining, by the client device, to restrict access to the application responsive to the deviation being greater than a threshold.
6. The method of claim 1 , wherein the data comprises one or more of a metric of a computing resource of the client device or a metric of the traffic.
7. A client device comprising:
one or more processors, coupled to memory and configured to:
monitor traffic of an application hosted on one or more remote computing devices and accessed via the client device;
provide, responsive to monitoring, data associated with traffic of the application as input to a model, the model configured to output identification of a predicted behavior of a user responsive to the input; and
cause access to the application by the user to be restricted responsive to the identification of the predicted behavior of the user from using the model;
wherein the one or more processors are further configured to use one or more weights with the model, the one or more weights determined based at least on the data.
8. The client device of claim 7 , wherein the one or more processors are further configured to access the application via browser within a client application of the client device.
9. The client device of claim 7 , wherein the one or more processors are further configured to generate, from monitoring, the data identifying one or more interactions of the user with the application.
10. The client device of claim 7 , wherein the one or more processors are further configured to determine a deviation from the predicted behavior and a behavior of the user measured from monitoring.
11. The client device of claim 10 , wherein the one or more processors are further configured to determine to restrict access to the application responsive to the deviation being greater than a threshold.
12. The client device of claim 7 , wherein the data comprises one or more of a metric of a computing resource of the client device or a metric of the traffic.
13. A non-transitory computer readable medium storing program instructions for causing one or more processors of a client device to:
monitor traffic of an application hosted on one or more remote computing devices and accessed via the client device;
provide, responsive to monitoring, data associated with traffic of the application as input to a model, the model configured to output identification of a predicted behavior of a user responsive to the input; and
cause access to the application by the user to be restricted responsive to the identification of the predicted behavior of the user from using the model;
wherein the program instructions further cause the one or more processors to use one or more weights with the model, the one or more weights determined based at least on the data.
14. The non-transitory computer readable medium of claim 13 , wherein the program instructions further cause the one or more processors to generate, from monitoring, the data identifying one or more interactions of the user with the application.
15. The non-transitory computer readable medium of claim 13 , wherein the program instructions further cause the one or more processors to determine a deviation from the predicted behavior and a behavior of the user measured from monitoring.
16. The non-transitory computer readable medium of claim 13 , wherein the program instructions further cause the one or more processors to determine to restrict access to the application responsive to the deviation being greater than a threshold.
17. The non-transitory computer readable medium of claim 13 , wherein the data comprises one or more of a metric of a computing resource of the client device or a metric of the traffic.