Adversarial Attacks on Continuous Authentication Security: A Dynamic Game Approach

Abstract

Identity theft through phishing and session hijacking attacks has become a major attack vector in recent years, and is expected to become more frequent due to the pervasive use of mobile devices. Continuous authentication based on the characterization of user behavior, both in terms of user interaction patterns and usage patterns, is emerging as an effective solution for mitigating identity theft, and could become an important component of defense-in-depth strategies in cyber-physical systems as well. In this paper, the interaction between an attacker and an operator using continuous authentication is modeled as a stochastic game. In the model, the attacker observes and learns the behavioral patterns of an authorized user whom it aims at impersonating, whereas the operator designs the security measures to detect suspicious behavior and to prevent unauthorized access while minimizing the monitoring expenses. It is shown that the optimal attacker strategy exhibits a threshold structure, and consists of observing the user behavior to collect information at the beginning, and then attacking (rather than observing) after gathering enough data. From the operator’s side, the optimal design of the security measures is provided. Numerical results are used to illustrate the intrinsic trade-off between monitoring cost and security risk, and show that continuous authentication can be effective in minimizing security risk.

This work was partly funded by the Swedish Civil Contingencies Agency (MSB) through the CERCES project and has received funding from the European Institute of Innovation and Technology (EIT). This body of the European Union receives support from the European Union’s Horizon 2020 research and innovation programme.