TITLE:
Malware Analysis and Classification: A Survey
AUTHORS:
Ekta Gandotra, Divya Bansal, Sanjeev Sofat
KEYWORDS:
Malware; Static Analysis; Dynamic Analysis; Machine Learning; Classification; Clustering
JOURNAL NAME:
Journal of Information Security,
Vol.5 No.2,
April
1,
2014
ABSTRACT:
One of the major and serious threats on the
Internet today is malicious software, often referred to as a malware. The
malwares being designed by attackers are polymorphic and metamorphic which have
the ability to change their code as they propagate. Moreover, the diversity and
volume of their variants severely undermine the effectiveness of traditional
defenses which typically use signature based techniques and are unable to
detect the previously unknown malicious executables. The variants of malware
families share typical behavioral patterns reflecting their origin and purpose.
The behavioral patterns obtained either statically or dynamically can be
exploited to detect and classify unknown malwares into their known families
using machine learning techniques. This survey paper provides an overview of
techniques for analyzing and classifying the malwares.