# Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

## TL;DR

This paper investigates the vulnerability of behavioral mouse dynamics authentication systems to adversarial attacks, proposing new attack strategies and analyzing their effectiveness in realistic scenarios.

## Contribution

The study introduces multiple generative adversarial attack methods on mouse dynamics authentication, analyzing their performance and proposing detection mechanisms in realistic settings.

## Key findings

- Adversarial mouse trajectories can successfully bypass authentication models.
- Imitation-based attacks often outperform surrogate-based attacks.
- Detection mechanisms can be effective against certain attack strategies.

## Abstract

Mouse dynamics is a potential means of authenticating users. Typically, the authentication process is based on classical machine learning techniques, but recently, deep learning techniques have been introduced for this purpose. Although prior research has demonstrated how machine learning and deep learning algorithms can be bypassed by carefully crafted adversarial samples, there has been very little research performed on the topic of behavioural biometrics in the adversarial domain. In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models. These generated mouse sequences will serve as the adversarial samples in the context of our experiments. We also present an analysis of the attack approaches we explored, explaining their limitations. In contrast to previous work, we consider the attacks in a more realistic and challenging setting in which an attacker has access to recorded user data but does not have access to the authentication model or its outputs. We explore three different attack strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we show that they are able to evade the functionality of the authentication models, thereby impacting their robustness adversely. We show that imitation-based attacks often perform better than surrogate-based attacks, unless, however, the attacker can guess the architecture of the authentication model. In such cases, we propose a potential detection mechanism against surrogate-based attacks.