Abstract
Mimicry attack is a fraud attack in which an attack can forge a legitimate user by imitating a legitimate user's media access control address or other identity credentials. In this paper, we propose an unsupervised model, namely DAMA, to detect mimicry attack by using the time-series of received signal strength (RSS) to detect the change of the device's location. The RSS of a device is related to its location, so it is difficult to forge. According to our experimental results, the F1 of DAMA is two times higher than state-of-the-art and the false alarm rate of DAMA is only 18% of state-of-the-art.
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