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A Survey on Automated Dynamic Malware-Analysis Techniques and Tools.

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Title: A Survey on Automated Dynamic Malware-Analysis Techniques and Tools.
Authors: Egele, Manuel, Scholte, Theodoor, Kirda, Engin, Kruegel, Christopher
Source: ACM Computing Surveys; Apr2012, Vol. 44 Issue 2, p6-6:42, 42p, 4 Diagrams, 1 Chart
Subject Terms: ANTIVIRUS software, MALWARE, COMPUTER security software, ANTI-malware (Computer software), COMPUTER virus prevention, COMPUTER security
Abstract: Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior. [ABSTRACT FROM AUTHOR]
Copyright of ACM Computing Surveys is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: A Survey on Automated Dynamic Malware-Analysis Techniques and Tools.
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  Data: <searchLink fieldCode="AR" term="%22Egele%2C+Manuel%22">Egele, Manuel</searchLink><br /><searchLink fieldCode="AR" term="%22Scholte%2C+Theodoor%22">Scholte, Theodoor</searchLink><br /><searchLink fieldCode="AR" term="%22Kirda%2C+Engin%22">Kirda, Engin</searchLink><br /><searchLink fieldCode="AR" term="%22Kruegel%2C+Christopher%22">Kruegel, Christopher</searchLink>
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  Data: ACM Computing Surveys; Apr2012, Vol. 44 Issue 2, p6-6:42, 42p, 4 Diagrams, 1 Chart
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  Data: Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ACM Computing Surveys is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2012
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