Stealing attack against controlled information, along with the increasing
number of information leakage incidents, has become an emerging cyber security
threat in recent years. Due to the booming development and deployment of
advanced analytics solutions, novel stealing attacks utilize machine learning
(ML) algorithms to achieve high success rate and cause a lot of damage.
Detecting and defending against such attacks is challenging and urgent so that
governments, organizations, and individuals should attach great importance to
the ML-based stealing attacks. This survey presents the recent advances in this
new type of attack and corresponding countermeasures. The ML-based stealing
attack is reviewed in perspectives of three categories of targeted controlled
information, including controlled user activities, controlled ML model-related
information, and controlled authentication information. Recent publications are
summarized to generalize an overarching attack methodology and to derive the
limitations and future directions of ML-based stealing attacks. Furthermore,
countermeasures are proposed towards developing effective protections from
three aspects — detection, disruption, and isolation.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Miao_Y/0/1/0/all/0/1">Yuantian Miao</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_C/0/1/0/all/0/1">Chao Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Pan_L/0/1/0/all/0/1">Lei Pan</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_Q/0/1/0/all/0/1">Qing-Long Han</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jun Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiang_Y/0/1/0/all/0/1">Yang Xiang</a>

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