Distributed monitoring, evaluation, and response for multiple devices

• US 9,753,796 B2
• Filed: 12/06/2013
• Issued: 09/05/2017
• Est. Priority Date: 12/06/2013
• Status: Active Grant

• Current Assignee: Lookout Incorporated
• Original Assignee: Lookout Incorporated
• Inventors: Mahaffey, Kevin Patrick, Wyatt, Timothy Micheal, Buck, Brian James, Hering, John Gunther, Gupta, Amit, Abey, Alex Cameron
• Primary Examiner(s): Hasan, Syed
• Application Number:
  US14/099,737
• Publication Number:
  US 20150163121A1
• Time in Patent Office: 1,369 Days
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0754 by exceeding limits
• CPC Class Codes: G06F 11/0766 Error or fault reporting or storing
• CPC Class Codes: G06F 11/3006 where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems multiprogramming arrangements G06F9/46; allocation of resources G06F9/50
• CPC Class Codes: G06F 11/3072 where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
• CPC Class Codes: G06F 11/3089 Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents software debugging using additional hardware using a specific debug interface G06F11/3656; performance evaluation by tracing or monitoring G06F11/3466
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04W 12/12 Detection or prevention of fraud
• CPC Class Codes: H04W 12/128 Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware

Abstract

Data is collected from a set of devices. The data is associated with the devices, mobile application programs (apps), web applications, users, or combinations of these. A norm is established using the collected data. The norm is compared with data collected from a particular device. If there is a deviation outside of a threshold deviation between the norm and the data collected from the particular device, a response is initiated.

Claims

1. A method comprising:

at a server, collecting observation data based on a first data collection policy from a plurality of devices, the collected observation data including information associated with device configuration, device state, and device behavior with regard to (1) an execution of an application on at least one of the plurality of devices and at least one of (2) a hardware or a firmware component on the at least one of the plurality of devices and (3) access to a network resource by the at least one of the plurality of devices;

at the server, determining a normal pattern of activity occurring on the plurality of devices by processing the collected observation data, the normal pattern of activity being associated with at least one of the device configuration, the device state, and the device behavior of the plurality of devices;

at the server, deriving a second data collection policy from the determined normal pattern of activity occurring on the plurality of devices, the second data collection policy being different than the first data collection policy;

at the server, collecting first device data based on the derived second data collection policy from a first device of the plurality of devices;

at the server comparing the normal pattern of activity occurring on the plurality of devices with a first pattern of activity occurring on the first device, the first pattern of activity being identified by the first device data;

at the server determining that a deviation between the normal pattern of activity and the first pattern of activity associated with the first device is outside of a threshold deviation; and

upon the determination, generating alert information by the server, wherein the alert information when processed causes performance of a first action on the first device.

2. The method of claim 1 wherein the action on the first device comprises:

blocking the first device from accessing a service.

3. The method of claim 1 wherein the action on the first device comprises:

transmitting to the first device instructions to uninstall an application program on the first device.

4. The method of claim 1 wherein the step of generating an alert further comprises:

transmitting a message to an administrator.

5. The method of claim 1 wherein the observation data collected comprises

a first set of observation data associated with an organization and collected from a first subset of the plurality of devices associated with the organization, and

a second set of observation data collected from a second subset of the plurality of device not associated with the organization.

6. A method comprising:

at a server, monitoring a plurality of devices for observation data based on a first data monitoring policy, the monitored observation data including information associated with device configuration, device state, and device behavior with regard to (1) an execution of an application on at least one of the plurality of devices and at least one of (2) a hardware or a firmware component on the at least one of the plurality of devices and (3) access to a network resource by the at least one of the plurality of devices;

at the server, establishing a normal pattern of activity occurring on the plurality of devices based on the monitored observation data, the normal pattern of activity being associated with at least one of the device configuration, the device state, and the device behavior of the plurality of devices;

at the server, deriving a second data monitoring policy from the determined normal pattern of activity occurring on the plurality of devices, the second data monitoring policy being different than the first data monitoring policy;

at the server, monitoring a first device of the plurality of devices for first device data based on the derived second data monitoring policy;

at the server, comparing the normal pattern of activity occurring on the plurality of devices with a first pattern of activity occurring on the first device, the first pattern of activity being identified by the monitored first device data;

at the server, determining that the first pattern of activity associated with the first device of the plurality of devices is outside of a threshold deviation from the normal pattern of activity; and

upon the determination, modifying the second data monitoring policy for monitoring of the first device by the server.

7. The method of claim 6 wherein the step of modifying the second data monitoring policy comprises:

increasing the monitoring of the first device.

8. The method of claim 6 wherein the step of modifying the second data monitoring policy comprises:

decreasing the monitoring of the first device.

9. The method of claim 6 wherein the normal pattern of activity indicates that a first event occurs during a first context, and the step of determining that activity on the first device is outside the normal pattern of activity comprises:

determining that the first event occurred on the first device during a second context, different from the first context.

10. The method of claim 6 wherein the step of determining that the first pattern of activity on the first device is outside the normal pattern of activity comprises:

receiving from the first device an indication that a shared library has been loaded on the first device from a memory card.

11. The method of claim 6 comprising:

generating a model of characteristics for a specific application program on the plurality of device; and

wherein the step of determining that the first pattern of activity on the first device is outside the normal pattern of activity comprises;

determining that the application program, on the first device and identified as being the same as the specific application program, has a characteristic that is not included in the model of characteristics.

12. The method of claim 6 wherein the step of monitoring the plurality of devices comprises:

monitoring a first subset of the plurality of devices for first events associated with the application program on the plurality of devices; and

monitoring a second subset of the plurality of devices for second events associated with the application program,

wherein the second subset of devices is not monitored for the first events, and the first subset of devices is not monitored for the second events.

13. A method comprising:

at a server, distributing first policies to a plurality of devices, the plurality of devices includes a first device;

at a server, receiving, from the plurality of devices, observation data responsive to the first policies for observation data, the received observation data including information associated with device configuration, device state, and device behavior with regard to (1) an execution of an application on at least one of the plurality of devices and at least one of (2) a hardware or a firmware component on the at least one of the plurality of devices and (3) access to a network resource by the at least one of the plurality of devices;

at the server, determining a normal pattern of activity occurring on the plurality of devices using the received observation data, the normal pattern of activity being associated with at least one of the device configuration, the device state, and the device behavior of the plurality of devices;

at the server, receiving first device data from a first device of the plurality of devices, the first device data being based on a second policy that has been derived from the normal pattern of activity occurring on the plurality of devices;

at the server, determining a first pattern of activity occurring on the first device using the received first device data;

at the server, comparing the normal pattern of activity with the first pattern of activity occurring on the first device;

at the server, determining that the first pattern of activity deviates from the normal pattern of activity outside of a threshold value; and

upon the determination, transmitting by the server, a second policy to the first device to replace the first policy distributed to the first device.

14. The method of claim 13 wherein the first policy specifies collection of a first level of detail, and the second policy specifies collection of a second level of detail greater than the first level of detail.

15. The method of claim 13 wherein the first policy specifies sampling data associated with the first device at a first frequency, and the second policy specifies sampling the data at a second frequency, greater than the first frequency.

16. The method of claim 13 wherein the received observation data does not comprise personally identifiable information (PII).

17. The method of claim 13 wherein a violation of the normal pattern of activity is detected when the normal pattern of activity specifies a first sequence of events, and the first pattern of activity specifies a second sequence of the events, different from the first sequence.