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Malicious Code Variant Detection : A Survey
Author(s) -
K Sreelakshmi,
E D Dileesh
Publication year - 2020
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset207141
Subject(s) - malware , computer science , cryptovirology , code (set theory) , computer security , malware analysis , software , programming language , set (abstract data type)
Malicious codes have become one of the major threats to computer systems. The malicious software which is also referred to as malware is designed by the attackers and can change their code as they propagate. The existing defense against malware is highly affected by the diversity and volume of malware variants that are being created rapidly. The variants of malware families exhibit typical behavioral patterns exhibiting their origin and purpose. The behavioral patterns can be exploited statically or dynamically to detect and classify malware into their known families. This paper provides a detailed survey of techniques to detect and classify malware into their respective families.

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