User identification using multifaceted footprints

• US 9,003,025 B2
• Filed: 07/05/2012
• Issued: 04/07/2015
• Est. Priority Date: 07/05/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Sailer, Reiner, Schales, Douglas Lee, Stoecklin, Marc, Wang, Ting, Christodorescu, Mihai
• Primary Examiner(s): Dalencourt, Yves
• Application Number:
  US13/542,422
• Publication Number:
  US 20140012973A1
• Time in Patent Office: 1,006 Days
• Field of Search: 709/224
• US Class Current: 709/224
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 2221/2101 Auditing as a secondary aspect
• CPC Class Codes: G06F 2221/2117 User registration
• CPC Class Codes: H04L 41/5058 Service discovery by the service manager
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A method for identifying an unknown user according to a plurality of facets of user activity in a plurality of contexts includes receiving a plurality of priors for the facets with respect to the contexts, receiving a plurality of footprints of known users, aggregating the footprints of the users to determine an ensemble prior, receiving a plurality of network traces relevant to an unknown user in a computer environment, matching the network traces against each of the footprints to determine a plurality of matches, aggregating the matches using the ensemble prior according to the facets and the contexts, and outputting a probable user identity for the unknown user.

Claims

1. A method for a computer having at least one processor for identifying an unknown user according to a plurality of facets of user activity in a plurality of contexts, the method comprising:

receiving a plurality of priors for the facets with respect to the contexts;

receiving a plurality of footprints of known users;

aggregating the footprints of the users to determine an ensemble prior;

receiving a plurality of network traces relevant to an unknown user in a computer environment;

matching the network traces against each of the footprints to determine a plurality of matches;

aggregating the matches using the ensemble prior according to the facets and the contexts; and

outputting a probable user identity for the unknown user.

2. The method of claim 1, wherein the network traces are received as an input stream, and the method further comprising performing the matching on the stream of network traces using an indexing structure.

3. The method of claim 1, further comprising outputting a plurality of user identities, ranked by probability.

4. The method of claim 1, wherein outputting further comprises outputting a sorted list of user identifies according to a probability of being the unknown user.

5. The method of claim 1, wherein each context indicated at least one of a time and a location of user activity.

6. The method of claim 1, wherein each facet is a measurement dimension of user activity.

7. A method for identifying an unknown user, the method comprising:

receiving a plurality of historical network traces of user activity;

extracting a footprint for each of a plurality of users from the historical network traces;

aggregating the footprints of the users to determine an ensemble prior;

receiving a plurality of network traces relevant to an unknown user in a computer environment;

matching the network traces against each of the footprints to determine a plurality of matches;

aggregating the matches using the ensemble prior according to a plurality of contexts and a plurality of facets; and

outputting a probable user identity for the unknown user.

8. The method of claim 7, wherein the network traces are received as an input stream, and the method further comprising performing the matching on the stream of network traces using an indexing structure.

9. The method of claim 7, further comprising outputting a plurality of user identities, ranked by probability.

10. The method of claim 7, wherein outputting further comprises outputting a sorted list of user identifies according to a probability of being the unknown user.

11. The method of claim 7, wherein each context indicated at least one of a time and a location of user activity.

12. The method of claim 7, wherein each facet is a measurement dimension of user activity.