DocumentCode :
153023
Title :
Analysis of machine learning methods on malware detection
Author :
Aydogan, Emre ; Sen, Satyaki
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey
fYear :
2014
fDate :
23-25 April 2014
Firstpage :
2066
Lastpage :
2069
Abstract :
Nowadays, one of the most important security threats are new, unseen malicious executables. Current anti-virus systems have been fairly successful against known malicious softwares whose signatures are known. However they are very ineffective against new, unseen malicious softwares. In this paper, we aim to detect new, unseen malicious executables using machine learning techniques. We extract distinguishing structural features of softwares and, employ machine learning techniques in order to detect malicious executables.
Keywords :
invasive software; learning (artificial intelligence); anti-virus systems; machine learning methods; malicious executables detection; malicious softwares; malware detection; security threats; software structural features; Conferences; Internet; Malware; Niobium; Signal processing; Software; machine learning; malware analysis and detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2014 22nd
Conference_Location :
Trabzon
Type :
conf
DOI :
10.1109/SIU.2014.6830667
Filename :
6830667
Link To Document :
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