Behavioural Analytics: Beyond Risk-based MFA

TL;DR

This paper presents a behavioral analytics system using keystroke dynamics to prevent unauthorized access with stolen credentials, achieving zero false positives in a proof-of-concept test.

Contribution

It introduces a novel keystroke dynamics-based behavioral analytics approach that enhances multi-factor authentication beyond risk-based methods.

Findings

Keystroke dynamics effectively distinguish legitimate users from impostors.

The system achieved zero false positives in testing with stolen credentials.

Behavioral analytics can significantly improve security against credential theft.

Abstract

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.