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