Methods and Systems for Using Behavioral Analysis Towards Efficient Continuous Authentication

Patent metadata

Field	Value
Publication number	US-2016110528-A1
Application number	US-201414514662-A
Country	US
Kind code	A1
Filing date	Oct 15, 2014
Priority date	Oct 15, 2014
Publication date	Apr 21, 2016
Grant date	—

Abstract

A computing device processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, and/or otherwise respond to malicious or performance-degrading behaviors of the computing device. As part of these operations, the processor may perform multifactor authentication operations that include determining one or more of a transaction type criticality value, a user confidence value, a software integrity confidence value, and a historical behavior value, using the one or more of these values to determine a number of authentication factors that are be evaluated when authenticating a user of the computing device, and authenticating the user by evaluating the determined number of authentication factors.

First claim

What is claimed is:

1 . A method of performing multifactor user authentication in a computing device, comprising:
determining in a processor of the computing device one or more of a transaction type criticality value, a user confidence value, a software integrity confidence value, and a historical behavior value; using one or more of the transaction type criticality value, the user confidence value, the software integrity confidence value, and the historical behavior value to determine a number of authentication factors that are be evaluated when authenticating a user of the computing device; and authenticating the user by evaluating the determined number of authentication factors.

2 . The method of claim 1 , further comprising:
using one or more of the transaction type criticality value, the user confidence value, the software integrity confidence value, and the historical behavior value to determine the authentication factors that are be evaluated when authenticating the user of the computing device.

3 . The method of claim 1 , further comprising:
monitoring hardware and software systems of the computing device to determine the computing device's current vulnerability to unauthorized use.

4 . The method of claim 1 , wherein determining one or more of the transaction type criticality value, the user confidence value, the software integrity confidence value, and the historical behavior value comprises:
monitoring activities of a software application to collect behavior information; generating a behavior vector that characterizes the monitored activities based on the collected behavior information; and applying the behavior vector to a classifier model to generate analysis results.

5 . The method of claim 4 , wherein the classifier model is a model of critical activity.

6 . The method of claim 5 , wherein the behavior vector is a multi-dimension vector data structure.

7 . The method of claim 1 , further comprising monitoring hardware and software systems of the computing device to learn over time a distinct way in which the user interacts with the computing device, wherein authenticating the user by evaluating the determined number of authentication factors comprises determining whether a behavior of a software application is consistent with the distinct way in which the user interacts with the computing device.

8 . The method of claim 1 , wherein determining the number of authentication factors that are to be evaluated comprises:
performing passive authentication operations to authenticate the user without requiring express user interaction for the purpose of authentication; determining a passive authentication confidence value that identifies the device level of confidence in an accuracy of the passive authentication operations; determining a criticality level value that identifies an importance or criticality of a process or software application operating on the computing device; comparing the passive authentication confidence value to the criticality level value to generate a comparison result that identifies whether a level of confidence in the passive authentication outweighs a level of criticality; and determining the number of authentication factors that are be evaluated when authenticating the user of the computing device based on the comparison result.

9 . A computing device, comprising:
a processor configured with processor-executable instructions to perform operations comprising:
determining one or more of a transaction type criticality value, a user confidence value, a software integrity confidence value, and a historical behavior value;
using one or more of the transaction type criticality value, the user confidence value, the software integrity confidence value, and the historical behavior value to determine a number of authentication factors that are be evaluated when authenticating a user of the computing device; and
authenticating the user by evaluating the determined number of authentication factors.

10 . The computing device of claim 9 , wherein the processor is configured with processor-executable instructions to perform operations further comprising:
using one or more of the transaction type criticality value, the user confidence value, the software integrity confidence value, and the historical behavior value to determine the authentication factors that are be evaluated when authenticating the user of the computing device.

11 . The computing device of claim 9 , wherein the processor is configured with processor-executable instructions to perform operations further comprising monitoring hardware and software systems to determine the computing device's current vulnerability to unauthorized use.

12 . The computing device of claim 9 , wherein the processor is configured with processor-executable instructions to perform operations such that determining one or more of the transaction type criticality value, the user confidence value, the software integrity confidence value, and the historical behavior value comprises:
monitoring activities of a software application to collect behavior information; generating a behavior vector that characterizes the monitored activities based on the collected behavior information; and applying the behavior vector to a classifier model to generate analysis results.

13 . The computing device of claim 12 , wherein the processor is configured with processor-executable instructions to perform operations such that applying the behavior vector to the classifier model to generate the analysis results comprises applying the behavior vector to a model of critical activity.

14 . The computing device of claim 13 , wherein the processor is configured with processor-executable instructions to perform operations such that applying the behavior vector to the model of critical activity comprises applying a multi-dimension vector data structure to the model of critical activity.

15 . The computing device of claim 9 , wherein:
the processor is configured with processor-executable instructions to perform operations further comprising monitoring hardware and software systems of the computing device to learn over time a distinct way in which the user interacts with the computing device; and the processor is configured with processor-executable instructions to perform operations such that authenticating the user by evaluating the determined number of authentication factors comprises determining whether a behavior of a software application is consistent with the distinct way in which the user interacts with the computing device.

16 . The computing device of claim 9 , wherein the processor is configured with processor-executable instructions to perform operations such that determining the number of authentication factors that are to be evaluated comprises:
performing passive authentication operations to authenticate the user without requiring express user interaction for the purpose of authentication; determining a passive authentication confidence value that identifies the device level of confidence in an accuracy of the passive authentication operations; determining a criticality level value that identifies an importance or criticality of a process or software application operating on the computing device; comparing the passive authentication confidence value to the criticality level value to generate a comparison result that identifies whether a level of confidence in the passive authentication outweighs a level of criticality; and determining the number of authentication factors that are be evaluated whe

Assignees

• Qualcomm Inc

Inventors

• Gupta Rajarshi
• Patne Satyajit Prabhakar

Classifications

• H04L63/1433

  Vulnerability analysis · CPC title

• H04L63/1425

  Traffic logging, e.g. anomaly detection · CPC title

• G06F2221/034

  Test or assess a computer or a system · CPC title

• H04W12/06

  Authentication · CPC title

• H04L63/105

  Multiple levels of security · CPC title