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Collusive attack that exploits biometric similarity difference and basic countermeasures


As one of the most popular IoT (Internet of Things) devices, smartphone stores sensitive personal information. As a result, authentication on smartphones attracts widespread attention in recent years. Sensor-based authentication methods have achieved excellent results due to their feasibility and high efficiency. However, the current work lacks comprehensive security verification, undetected potential vulnerabilities are likely to be leveraged to launch attacks on these authentication approaches. We propose a novel attack to evaluate the reliability and robustness of the existing authentication methods. The basic idea behind our strategy is that the system has its authentication error; we elaborately analyze the false-negative samples to summarize its vulnerable properties and leverage such vulnerabilities to design our attack. The experiment result proves the feasibility of our attack and also demonstrates the drawbacks of the existing approaches. In addition, we propose a corresponding protect approach to defend against this attack, of which the scheme has the self-learning ability to update according to the newly detected attacks. Compared with authentications using multiple sensors, we only adopt a single accelerometer to achieve better performance, showing the convenience and effectiveness of our system.

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Supported by the National Natural Science Foundation of China (Grant No. 62002278).

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Correspondence to Yao Wang.

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Lyu, P., Cai, W. & Wang, Y. Collusive attack that exploits biometric similarity difference and basic countermeasures. Wireless Netw (2022).

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  • Gait authentication
  • Wearable sensors
  • Impersonation attack
  • Collusive attack