NDLI: Packer classifier based on PE header information

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Packer classifier based on PE header information

Content Provider	ACM Digital Library
Author	Duan, Jiayi Vasudevan, Shobha Bailey, Michael Jin, Qiao
Abstract	Run-time binary packers are used in malware manufacturing to obfuscate the contents of the executable files. Such packing has proved an obstacle for antivirus software that relies on signatures, as the binary contents of packed malware often bears no resemblance to the original code on which the signature was generated. A naive approach, then, is to first attempt to unpack the malware before applying a signature. Unfortunately, malware authors make use of automated tools that drastically reduce the cost of constructing new packers, and as a result, attackers routinely use previously unseen packer when releasing new malware. As a first step towards addressing this problem, we seek to build a binary program classifier that can differentiate packers and identify new packers as they emerge. We hypothesize that programs generated from the same packer share many common attributes (e.g., PE header fields) and that these may be used for packer identification. Preliminary work shows that for some packers, we may be able to build effective classifiers. This is only the first step in a line of research that seeks to identify new packers, automate their unpacking, and ultimately track new versions of malware as they emerge.
Starting Page	1
Ending Page	2
Page Count	2
File Format	PDF
ISBN	9781450333764
DOI	10.1145/2746194.2746213
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-04-21
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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