Behavioural Analytics: Beyond Risk-based MFA
This addresses security for organizations facing credential theft, offering a novel approach to authentication.
This paper tackles the problem of attackers using compromised credentials to gain unauthorized access by proposing a behavioral analytics system that uses keystroke dynamics to grant or deny access. In tests, the system prevented all illegitimate users with correct credentials from accessing the system, demonstrating zero tolerance to false positives.
This paper investigates how to effectively stop an attacker from using compromised user credentials to gain authorized entry to systems that they are otherwise not authorised to access. The proposed solution extends previous work to move beyond a risk-based multi-factor authentication system. It adds a behavioural analytics component that uses keystroke dynamics to grant or deny users access. Given the increasing number of compromised user credential stores, we make the assumption that criminals already know the user credentials. Hence, to test our solution, users were given authentic user credentials and asked to login to our proof-of-concept. Despite the fact that all illegitimate users in our test cases were given the correct user credentials for legitimate users, none of these were granted access by the system. This demonstrates zero- tolerance to false positives. The results demonstrate the uniqueness of keystroke dynamics and its use to prevent users with stolen credentials from accessing systems they are not authorized to access.