Explainable Black-Box Attacks Against Model-based Authentication

Understand

Establishing unique identities for both humans and end systems has been an active research problem in the security community, giving rise to innovative machine learning-based authentication techniques.

• Although such techniques offer an automated method to establish identity, they have not been vetted against sophisticated attacks that target their core machine learning technique.
• This paper demonstrates that mimicking the unique signatures generated by host fingerprinting and biometric authentication systems is possible.
• We expose the ineffectiveness of underlying machine learning classification models by constructing a blind attack based around the query synthesis framework and utilizing Explainable-AI (XAI) techniques.