| Summary: | Malware detection refers to the classification of a software as malicious or
benign. Many attempts, employing diverse techniques, have been tried to
tackle this issue. In the present thesis, we present a graph-based solution to
the malware detection problem, which implements resources extraction from
executable samples and applies machine learning algorithms to those resources
so as to decide on the nature of the executable (malicious or benign). Given
an unknown Windows executable sample, we first extract the calls that the
sample makes to the Windows Application Programming Interface (API) and
arrange them in the form of an API Call Graph, based on which, an Abstract
API Call Graph is constructed. Subsequently, using a Random Walk Graph
Kernel, we are able to quantify the similarity between the graph of the
unknown sample and the corresponding graphs hailing from a labeled dataset
of known samples (benign and malicious Windows executables), in order to
carry out the binary classification using Support Vector Machines. Following
the aforementioned process, we achieve accuracy levels up to 98.25%, using a
substantially smaller dataset than the one proposed by similar efforts, while
being considerably more efficient in time and computational power.
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