IP Library Granted Patent US 12,476,968
Granted Patent B2
US 12,476,968 · App. 18/464,623 · Granted Nov 18, 2025

Risk-based multi-factor authentication

Inventors: Jason Crabtree (Vienna, VA); Andrew Sellers (Monument, CO); Ian MacLeod (Arlington, VA)
Assignee: QOMPLX LLC
H04L63/0861H04L43/04H04L63/083H04L63/0876H04L63/105H04L63/1433H04L63/1408H04L2463/082
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,476,968
App. No.
18/464,623
Granted
Nov 18, 2025
Kind
B2
Abstract

A system for risk-based multi-factor authentication having a multi-dimensional time series data server configured to monitor and record a network's traffic data and to serve the traffic data to other modules and a directed computation graph module configured to receive network traffic data from the multi-dimensional time series data server, determine a network traffic baseline from the network traffic data, and determine a verification score needed before granting access based at least in part by the network traffic baseline. A plurality of verification methods build up a user's verification score to required level to gain access.

Claims (39)

1 . A system for risk-based multi-factor authentication, comprising:

a computing device comprising a memory and a processor, the computing device being connected to a network; and

a plurality of programming instructions stored in the memory of, and operable on the processor of, the computing device, wherein the plurality of programming instructions, when operating on the processor, causes the computing device to:

establish a baseline network profile associated with a user from time series traffic data for the network;

maintain an evolving network profile associated with the user based on network traffic and activities of the user;

analyze the network traffic using machine learning techniques to identify a cybersecurity threat to the network;

when the user attempts to access a network resource, require a response to a first additional verification at a required verification level from the user, wherein the required verification level is determined, at least in part, on the evolving network profile associated with the user, a type of the network resource, the identified cybersecurity threat, and a location of the attempted access;

determine, based on receipt of the required response to the first additional verification, whether the required response meets the required verification level; and

if the required response meets the required verification level, grant the requested access to the network resource.

2 . The system of claim 1 , wherein:

if the required verification level has not been met:

require a response to a second additional verification from the user;

determine whether the required response to the second additional verification meets the required verification level; and

if the required response meets the required verification level, grant the requested access to the network resource.

3 . The system of claim 1 , wherein the required verification level is determined further based on a security level associated with the network resource.

4 . The system of claim 1 , wherein the time series data further comprises the origin of the attempted access.

5 . The system of claim 1 , wherein the required verification level includes a visual verification of a credential associated with the user.

6 . The system of claim 1 , wherein the required verification level includes a biometric verification of a credential associated with the user.

7 . The system of claim 1 , wherein the required verification level includes information obtained from untrusted parties.

8 . The system of claim 1 , wherein the required verification level includes information associated with a user device associated with the user that is used to access the network resource.

9 . A method for risk-based multi-factor authentication, comprising the steps of:

using a plurality of programming instructions stored in the memory of, and operable on the processor of, a computing device:

establishing a baseline network profile associated with a user from time series traffic data for the network;

maintaining an evolving network profile associated with the user based on network traffic and activities of the user;

analyzing the network traffic using machine learning techniques to identify a cybersecurity threat to the network;

when the user attempts to access a network resource, requiring a response to a first additional verification at a required verification level from the user, wherein the required verification level is determined, at least in part, on the evolving network profile associated with the user, a type of the network resource, the identified cybersecurity threat, and a location of the attempted access;

determining, based on receipt of the required response to the first additional verification, whether the required response meets the required verification level; and

if the required response meets the required verification level, granting the requested access to the network resource.

10 . The method of claim 9 , wherein:

if the required verification level has not been met:

requiring a response to a second additional verification from the user;

determining whether the required response to the second additional verification meets the required verification level; and

if the required response meets the required verification level, granting the requested access to the network resource.

11 . The method of claim 9 , wherein the required verification level is determined further based on a security level associated with the network resource.

12 . The method of claim 9 , wherein the time series data further comprises the origin of the attempted access.

13 . The method of claim 9 , wherein the required verification level includes a visual verification of a credential associated with the user.

14 . The method of claim 9 , wherein the required verification level includes a biometric verification of a credential associated with the user.

15 . The method of claim 9 , wherein the required verification level includes information obtained from untrusted parties.

16 . The method of claim 9 , wherein the required verification level includes information associated with a user device associated with the user that is used to access the network resource.

Assignments (4)
CHANGE OF NAME Recorded Apr 16, 2024
From: FRACTAL INDUSTRIES, INC.
To: QOMPLX, INC.
Reel/Frame 067129/0123 →
CHANGE OF NAME Recorded Apr 16, 2024
From: QPX LLC
To: QOMPLX LLC
Reel/Frame 067129/0142 →
PATENT ASSIGNMENT AGREEMENT TO ASSET PURCHASE AGREEMENT Recorded Feb 20, 2024
From: QOMPLX, INC.
To: QPX LLC
Reel/Frame 066632/0200 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 28, 2024
From: CRABTREE, JASON; SELLERS, ANDREW; MACLEOD, IAN
To: FRACTAL INDUSTRIES, INC.
Reel/Frame 066270/0157 →
Continuity (15)
Continuation 17539137 · Nov 30, 2021
Continuation 16856827 · Apr 23, 2020
Continuation 15790860 · Oct 23, 2017
Continuation In Part 15616427 · Jun 7, 2017
Continuation In Part 14925974 · Oct 28, 2015
Continuation In Part 15237625 · Aug 15, 2016
Continuation In Part 15206195 · Jul 8, 2016
Continuation In Part 15186453 · Jun 18, 2016
Continuation In Part 15166158 · May 26, 2016
Continuation In Part 15141752 · Apr 28, 2016
Continuation In Part 15091563 · Apr 5, 2016
Continuation In Part 14986536 · Dec 31, 2015
Continuation In Part 14925974 · Oct 28, 2015
Provisional Application 62574708 · Oct 19, 2017
Related Publication 20240080318A1 · Mar 7, 2024
References Cited (62)
US 6256544B1 · Weissinger · 2001 [cited by examiner]
US 6477572B1 · Elderton · 2002 [cited by examiner]
US 6857073B2 · French · 2005 [cited by examiner]
US 7139747B1 · Najork · 2006 [cited by examiner]
US 7171515B2 · Ohta · 2007 [cited by examiner]
US 7227948B2 · Ohkuma · 2007 [cited by examiner]
US 7310632B2 · Meek · 2007 [cited by examiner]
US 7322044B2 · Hrastar · 2008 [cited by examiner]
US 7530105B2 · Gilbert · 2009 [cited by examiner]
US 7546333B2 · Alon · 2009 [cited by examiner]
US 7685296B2 · Brill · 2010 [cited by examiner]
US 7818224B2 · Boerner · 2010 [cited by examiner]
US 7818417B2 · Ginis · 2010 [cited by examiner]
US 7925561B2 · Xu · 2011 [cited by examiner]
US 8069190B2 · McColl · 2011 [cited by examiner]
US 8346753B2 · Hayes · 2013 [cited by examiner]
US 8457996B2 · Winkler · 2013 [cited by examiner]
US 8751867B2 · Marvasti · 2014 [cited by examiner]
US 8832840B2 · Zhu · 2014 [cited by examiner]
US 8949960B2 · Berkman · 2015 [cited by examiner]
US 9069976B2 · Toole · 2015 [cited by examiner]
US 9110706B2 · Yu · 2015 [cited by examiner]
US 9152727B1 · Balducci · 2015 [cited by examiner]
US 9256735B2 · Stute · 2016 [cited by examiner]
US 9400962B2 · Prasad · 2016 [cited by examiner]
US 9466041B2 · Simitsis · 2016 [cited by examiner]
US 9558220B2 · Nixon · 2017 [cited by examiner]
US 9560065B2 · Neil · 2017 [cited by examiner]
US 9652538B2 · Shivaswamy · 2017 [cited by examiner]
US 9774522B2 · Vasseur · 2017 [cited by examiner]
US 10044726B2 · Dulkin · 2018 [cited by examiner]
US 10191768B2 · Bishop · 2019 [cited by examiner]
US 10210246B2 · Stojanovic · 2019 [cited by examiner]
US 10216485B2 · Misra · 2019 [cited by examiner]
US 10270748B2 · Briceno · 2019 [cited by examiner]
US 10333992B2 · Kinder · 2019 [cited by examiner]
US 10643144B2 · Bowers · 2020 [cited by examiner]
US 10846391B1 · Bonney · 2020 [cited by examiner]
US 20030033526A1 · French · 2003 [cited by examiner]
US 20040098610A1 · Hrastar · 2004 [cited by examiner]
US 20050000165A1 · Dischinat · 2005 [cited by examiner]
US 20050165822A1 · Yeung · 2005 [cited by examiner]
US 20070226796A1 · Gilbert · 2007 [cited by examiner]
US 20110307467A1 · Severance · 2011 [cited by examiner]
US 20130111592A1 · Zhu · 2013 [cited by examiner]
US 20130117852A1 · Stute · 2013 [cited by examiner]
US 20140250153A1 · Nixon · 2014 [cited by examiner]
US 20140324521A1 · Mun · 2014 [cited by examiner]
US 20140359552A1 · Misra · 2014 [cited by examiner]
US 20150020199A1 · Neil · 2015 [cited by examiner]
US 20150319156A1 · Guccione · 2015 [cited by examiner]
US 20160006629A1 · Ianakiev · 2016 [cited by examiner]
US 20160012235A1 · Lee · 2016 [cited by examiner]
US 20160275123A1 · Lin · 2016 [cited by examiner]
US 20170090893A1 · Aditya · 2017 [cited by examiner]
US 20180082304A1 · Summerlin · 2018 [cited by examiner]
US 20230035505A1 · Olds · 2023 [cited by examiner]
WO 2014159150A1 · 2014 [cited by applicant]
WO 2017075543A1 · 2017 [cited by applicant]
Huang, Alex, A Comparison of Value at Risk Approaches and a New Method with Extreme Value Theory and Kernel Estimator. [cited by applicant]
Marozzo, Fabrizio; Talia, Domenico; Trunfio, Paolo; P2P-MapReduce—Parallel Data Processing in Dynamic Cloud Environments, Journal of Computer and System Sciences, 78 (2012) 1382-1402. [cited by applicant]
Simonian, Joseph, Davis, Josh., Robust Value-At-Risk: An Information Theoretic Approach, Applied Economic Letters, 2010, 17, 1551-1553. [cited by applicant]