System and method for continuous passwordless authentication across trusted devices
Aspects of the disclosure provide techniques for using egocentric and allocentric information for providing and restricting access to a secure network and its assets to a user entity. The system may include capturing user habits and fingerprinting with ability to detect abnormalities through artificial intelligence/machine learning (AIML) using mobile and wearable device applications.
1. A system for monitoring a secure network comprising:
a processor coupled to a network interface, the processor configured to:
capture contextual and behavioral factors of a user entity interacting with a client device, wherein the contextual and behavioral factors include user entity behavior, characteristics of the client device, and characteristics of a client device browser;
capture network contextual factors of the secure network upon which the client device is attempting to login, wherein the network contextual and behavioral factors include computer devices which are logged onto the secure network;
calculate a trust score based on both the user entity contextual factors and the network contextual factors throughout an active session of the client device; and
monitor the decay of the trust score over time to determine whether the active session should continue.
2. The system of claim 1 , wherein the trust score when not met can result in a proper smart step up authentication request as a multi-factor authentication request.
3. The system of claim 1 , wherein the processor is further configured to: capture behavioral controls in calculating the trust score.
4. The system of claim 1 , wherein the trust score can increase depending on successful login of client device applications.
5. The system of claim 1 , wherein the trust score will decay based on at least one of the group consisting of: predetermined time intervals, workflow abnormalities and applications pane.
6. The system of claim 1 , the processor further configured to: verify the identity of the user entity using out-of-band confirmation; and reset the trust score based on successful out-of-band confirmation.
7. The system of claim 1 , the processor further configured to: lock the user entity out of the client device when the trust score reaches a predetermined level.
8. The system of claim 1 , the processor further configured to: dynamically changing the level of assurance of a user entity based on the trust score.
9. The system of claim 1 , wherein the user entity contextual factors and network contextual factors may include allocentric factors.
10. The system of claim 1 , wherein the network contextual factors include using peer trust to discover devices on the network to determine whether to trust a user entity logon.
11. The system of claim 1 ,
wherein the contextual factors and network contextual factors may include at least one of the group of egocentric or allocentric factors comprising:
a mobile device model, a mobile device hardware configuration, a mobile device operating system, mobile device applications, a mobile device web browser version, a service set identifier (SSID) of the network WiFi, network information such as an Internet Protocol (IP) address, object classes transferred, screen size, font size, language, user entity habits including speed and style of user entity keyboard entry, mouse strokes, screen touch, an adjacent companion mobile device in proximity, biobehavioral data derived from the user entity such as walking gait, trusted locations of the user entity, haptic-tactic factors derived from hardware sensors embedded inside the client device, various specialized sensor data captured by the hardware such as ambient noise, temperature, discrete movement and location of the mobile device, walking and exercise habits of the user entity, user entity location, user entity driving, transactions on mobile including services, applications used and their frequency and duration including calls, browsing, use of applications, and exercise routines.
12. A method for monitoring a secure network comprising:
capturing contextual and behavioral factors of a user entity interacting with a client device, wherein the contextual and behavioral factors include user entity behavior, characteristics of the client device, and characteristics of a client device browser;
capturing network contextual factors of the secure network upon which the client device is attempting to login, wherein the network contextual and behavioral factors include computer devices which are logged onto the secure network;
calculating a trust score based on both the user entity contextual factors and the network contextual factors throughout an active session of the client device; and
monitoring the decay of the trust score over time to determine whether the active session should continue.
13. The method of claim 12 , stepping up the authentication request as a multi-factor authentication request when the trust score is not met.
14. The method of claim 12 , further comprising: capturing behavioral controls in calculating the trust score.
15. The method of claim 12 , further comprising: increasing the trust score depending on successful login of client device applications.
16. The method of claim 12 , further comprising: decaying the trust score at predetermined time intervals.
17. The method of claim 12 , further comprising: decaying the trust score based on workflow abnormalities.
18. The method of claim 12 , further comprising: decaying the trust score based on an applications pane.
19. The method system of claim 12 , wherein the user entity contextual factors and network contextual factors may include allocentric factors.
20. The system of claim 12 , wherein the network contextual factors include using peer trust to discover devices on the network to determine whether to trust a user entity logon.