Exploiting Behavioral Side Channels in Observation Resilient Cognitive Authentication Schemes

Author:

Zhao Benjamin Zi Hao1ORCID,Asghar Hassan Jameel2,Kaafar Mohamed Ali2,Trevisan Francesca3,Yuan Haiyue3

Affiliation:

1. University of New South Wales and Data61, CSIRO, Australia

2. Macquarie University and Data61, CSIRO, Australia

3. University of Surrey, United Kingdom

Abstract

Observation Resilient Authentication Schemes (ORAS) are a class of shared secret challenge–response identification schemes where a user mentally computes the response via a cognitive function to authenticate herself such that eavesdroppers cannot readily extract the secret. Security evaluation of ORAS generally involves quantifying information leaked via observed challenge–response pairs. However, little work has evaluated information leaked via human behavior while interacting with these schemes. A common way to achieve observation resilience is by including a modulus operation in the cognitive function. This minimizes the information leaked about the secret due to the many-to-one map from the set of possible secrets to a given response. In this work, we show that user behavior can be used as a side channel to obtain the secret in such ORAS. Specifically, the user’s eye-movement patterns and associated timing information can deduce whether a modulus operation was performed (a fundamental design element) to leak information about the secret. We further show that the secret can still be retrieved if the deduction is erroneous, a more likely case in practice. We treat the vulnerability analytically and propose a generic attack algorithm that iteratively obtains the secret despite the “faulty” modulus information. We demonstrate the attack on five ORAS and show that the secret can be retrieved with considerably less challenge–response pairs than non-side-channel attacks (e.g., algebraic/statistical attacks). In particular, our attack is applicable on Mod10, a one-time-pad-based scheme, for which no non-side-channel attack exists. We field test our attack with a small-scale eye-tracking user study.

Funder

COMMANDO-HUMANS project

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,General Computer Science

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