  Deep learning and other machine learning approaches are deployed to many
systems related to Internet of Things or IoT. However, it faces challenges that
adversaries can take loopholes to hack these systems through tampering history
data. This paper first presents overall points of adversarial machine learning.
Then, we illustrate traditional methods, such as Petri Net cannot solve this
new question efficiently. To help IoT data analysis more efficient, we propose
a retrieval method based on deep learning (recurrent neural network). Besides,
this paper presents a research on data retrieval solution to avoid hacking by
adversaries in the fields of adversary machine leaning. It further directs the
new approaches in terms of how to implementing this framework in IoT settings
based on adversarial deep learning.
