437 research outputs found
Survey of Machine Learning Techniques for Malware Analysis
Coping with malware is getting more and more challenging, given their
relentless growth in complexity and volume. One of the most common approaches
in literature is using machine learning techniques, to automatically learn
models and patterns behind such complexity, and to develop technologies for
keeping pace with the speed of development of novel malware. This survey aims
at providing an overview on the way machine learning has been used so far in
the context of malware analysis. We systematize surveyed papers according to
their objectives (i.e., the expected output, what the analysis aims to), what
information about malware they specifically use (i.e., the features), and what
machine learning techniques they employ (i.e., what algorithm is used to
process the input and produce the output). We also outline a number of problems
concerning the datasets used in considered works, and finally introduce the
novel concept of malware analysis economics, regarding the study of existing
tradeoffs among key metrics, such as analysis accuracy and economical costs
Probabilistic Naming of Functions in Stripped Binaries
Debugging symbols in binary executables carry the names of functions and global variables. When present, they greatly simplify the process of reverse engineering, but they are almost always removed (stripped) for deployment. We present the design and implementation of punstrip, a tool which combines a probabilistic fingerprint of binary code based on high-level features with a probabilistic graphical model to learn the relationship between function names and program structure. As there are many naming conventions and developer styles, functions from different applications do not necessarily have the exact same name, even if they implement the exact same functionality. We therefore evaluate punstrip across three levels of name matching: exact; an approach based on natural language processing of name components; and using Symbol2Vec, a new embedding of function names based on random walks of function call graphs. We show that our approach is able to recognize functions compiled across different compilers and optimization levels and then demonstrate that punstrip can predict semantically similar function names based on code structure. We evaluate our approach over open source C binaries from the Debian Linux distribution and compare against the state of the art
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