  The idea of applying machine learning(ML) to solve problems in security
domains is almost 3 decades old. As information and communications grow more
ubiquitous and more data become available, many security risks arise as well as
appetite to manage and mitigate such risks. Consequently, research on applying
and designing ML algorithms and systems for security has grown fast, ranging
from intrusion detection systems(IDS) and malware classification to security
policy management(SPM) and information leak checking. In this paper, we
systematically study the methods, algorithms, and system designs in academic
publications from 2008-2015 that applied ML in security domains. 98 percent of
the surveyed papers appeared in the 6 highest-ranked academic security
conferences and 1 conference known for pioneering ML applications in security.
We examine the generalized system designs, underlying assumptions,
measurements, and use cases in active research. Our examinations lead to 1) a
taxonomy on ML paradigms and security domains for future exploration and
exploitation, and 2) an agenda detailing open and upcoming challenges. Based on
our survey, we also suggest a point of view that treats security as a game
theory problem instead of a batch-trained ML problem.
