  Implicit authentication (IA) transparently authenticates users by utilizing
their behavioral data sampled from various sensors. Identifying the
illegitimate user through constantly analyzing current users' behavior, IA adds
another layer of protection to the smart device. Due to the diversity of human
behavior, the existing research works tend to simultaneously utilize many
different features to identify users, which is less efficient. Irrelevant
features may increase system delay and reduce the authentication accuracy.
However, dynamically choosing the best suitable features for each user
(personal features) requires a massive calculation, especially in the real
environment. In this paper, we proposed EchoIA to find personal features with a
small amount of calculation by utilizing user feedback. In the authentication
phase, our approach maintains the transparency, which is the major advantage of
IA. In the past two years, we conducted a comprehensive experiment to evaluate
EchoIA. We compared it with other state-of-the-art IA schemes in the aspect of
authentication accuracy and efficiency. The experiment results show that EchoIA
has better authentication accuracy (93\%) and less energy consumption (23-hour
battery lifetimes) than other IA schemes.
