DYNAMIC MOBILE APPLICATION CLASSIFICATION

• US 20130183951A1
• Filed: 01/12/2012
• Published: 07/18/2013
• Est. Priority Date: 01/12/2012
• Status: Abandoned Application

• Current Assignee: Lionic Corp.
• Original Assignee: Lionic Corp.
• Inventors: Chien, Shih-Wei
• Application Number:
  US13/348,654
• Publication Number:
  US 20130183951A1
• Time in Patent Office: Days
• US Class Current: 455/418
• CPC Class Codes: H04W 4/50 Service provisioning or reconfiguring
• CPC Class Codes: H04W 4/60 Subscription-based services using application servers or record carriers, e.g. SIM application toolkits

Abstract

In accordance with embodiments of the present disclosure, a process for classifying a mobile application is provided. The process may detect, by an application classification module, a mobile application located on a mobile device. The process may further extract, by the application classification module, a set of embedded data from the mobile application; and obtain a classification for the mobile application by analyzing the set of embedded data using a pattern and training set database.

Claims

1. A method for classifying a mobile application, comprising:

detecting, by an application classification module, a mobile application located on a mobile device;

extracting, by the application classification module, a set of embedded data from the mobile application; and

obtaining, by the application classification module, a classification for the mobile application by analyzing the set of embedded data using a pattern and training set database.

2. The method as recited in claim 1, further comprising:

upon a determination that the classification is below a predetermined threshold, preventing the mobile application from installing or executing on the mobile device.

3. The method as recited in claim 1, wherein the obtaining the classification comprises:

identifying, by the application classification module running on the mobile device, a data type for the set of embedded data; and

generating the classification by invoking a classifier corresponding to the data type for analyzing the set of embedded data.

4. The method as recited in claim 3, wherein a URL classifier generates the classification by comparing a URL extracted from the set of embedded data with URLs stored in the pattern and training set database.

5. The method as recited in claim 3, wherein a text classifier generates the classification by comparing a text string extracted from the set of embedded data with the pattern and training set database.

6. The method as recited in claim 3, wherein a graphic classifier generates the classification by comparing an image extracted from the set of embedded data with the pattern and training set database.

7. The method as recited in claim 3, wherein a video classifier generates the classification by comparing a video extracted from the set of embedded data with the pattern and training set database.

8. The method as recited in claim 1, wherein the obtaining the classification comprises:

transmitting, by the application classification module, the set of embedded data to a remote classification server via a mobile network; and

receiving, from the remote classification server, the classification for the mobile application.

9. The method as recited in claim 1, wherein the extracting the set of embedded data comprises:

monitoring, by a dynamic data extractor, the mobile application utilizing a set of application data; and

extracting, by the dynamic data extractor, the set of embedded data from the set of application data.

10. The method as recited in claim 9, wherein the monitoring the mobile application comprises:

monitoring storage data being accessed by the mobile application as the set of application data.

11. The method as recited in claim 9, wherein the monitoring the mobile application comprises:

intercepting network data being transmitted by the mobile application as the set of application data.

12. The method as recited in claim 9, wherein the dynamic data extractor is executing on the mobile device while monitoring the mobile application accessing the set of application data via a storage on the mobile device, and monitoring the mobile application transmitting the set of application data via a network interface on the mobile device.

13. The method as recited in claim 9, wherein the dynamic data extractor is executing on a mobile device hypervisor and has access to a storage on the mobile device that is utilized by the mobile application for storing the set of application data, and access to a network interface on the mobile device that is utilized by the mobile application for transmitting the set of application data.

14. A method for classifying a mobile application running on a mobile device, comprising:

obtaining, by a classification collection module, a first classification for the mobile application and a set of embedded data extracted from the mobile application;

processing, by the classification collection module, the set of embedded data to extract a set of patterns and features; and

storing, by the classification collection module, the set of patterns and features to a pattern and training set database, wherein the pattern and training set database is used by an application classification module to classify the mobile application.

15. The method as recited in claim 14, further comprising:

generating a second classification for the mobile application based on the first classification and the pattern and training set database.

16. The method as recited in claim 14, further comprising:

associating the second classification with the set of patterns and features in the pattern and training set database.

17. A system configured to classify a mobile application running on a mobile device, comprising:

a data extractor for monitoring the mobile application and extracting a set of embedded data from the mobile application; and

a classifier coupled with the data extractor for receiving the set of embedded data from the data extractor, and generating a classification for the mobile application based on the set of embedded data.

18. The system as recited in claim 17 wherein the classifier is a URL classifier, a text classifier, a graphic classifier, or a video classifier.

19. The system as recited in claim 17, wherein the data extractor extracts the set of embedded data by statically evaluating the mobile application'"'"'s installation files.

20. The system as recited in claim 17, wherein the data extractor extracts the set of embedded data by dynamically evaluating the mobile application being executed on the mobile device.