Abstract
Malware landscape has been dramatically elevated over the last decade. The main reason of the increase is that new malware variants can be produced easily using simple code obfuscation techniques. Once the obfuscation is applied, the malware can change their syntactics while preserving semantics, and bypass anti-virus (AV) scanners. Malware authors, thus, commonly use the code obfuscation techniques to generate metamorphic malware. Nevertheless, signature based AV techniques are limited to detect the metamorphic malware since they are commonly based on the syntactic signature matching. In this paper, we propose BinGraph, a new mechanism that accurately discovers metamorphic malware. BinGraph leverages the semantics of malware, since the mutant malware is able to manipulate their syntax only. To this end, we first extract API calls from malware and convert to a hierarchical behavior graph that represents with identical 128 nodes based on the semantics. Later, we extract unique subgraphs from the hierarchical behavior graphs as semantic signatures representing common behaviors of a specific malware family. To evaluate BinGraph, we analyzed a total of 827 malware samples that consist of 10 malware families with 1,202 benign binaries. Among the malware, 20% samples randomly chosen from each malware family were used for extracting semantic signatures, and rest of them were used for assessing detection accuracy. Finally, only 32 subgraphs were selected as the semantic signatures. BinGraph discovered malware variants with 98% of detection accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2012 7th International Conference on Malicious and Unwanted Software, Malware 2012 |
| Pages | 104-111 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
| Event | 2012 7th International Conference on Malicious and Unwanted Software, Malware 2012 - Fajardo, PR, United States Duration: 2012 Oct 16 → 2012 Oct 18 |
Other
| Other | 2012 7th International Conference on Malicious and Unwanted Software, Malware 2012 |
|---|---|
| Country | United States |
| City | Fajardo, PR |
| Period | 12/10/16 → 12/10/18 |
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ASJC Scopus subject areas
- Software
Cite this
BinGraph : Discovering mutant malware using hierarchical semantic signatures. / Kwon, Jonghoon; Lee, Heejo.
Proceedings of the 2012 7th International Conference on Malicious and Unwanted Software, Malware 2012. 2012. p. 104-111 6461015.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - BinGraph
T2 - Discovering mutant malware using hierarchical semantic signatures
AU - Kwon, Jonghoon
AU - Lee, Heejo
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Malware landscape has been dramatically elevated over the last decade. The main reason of the increase is that new malware variants can be produced easily using simple code obfuscation techniques. Once the obfuscation is applied, the malware can change their syntactics while preserving semantics, and bypass anti-virus (AV) scanners. Malware authors, thus, commonly use the code obfuscation techniques to generate metamorphic malware. Nevertheless, signature based AV techniques are limited to detect the metamorphic malware since they are commonly based on the syntactic signature matching. In this paper, we propose BinGraph, a new mechanism that accurately discovers metamorphic malware. BinGraph leverages the semantics of malware, since the mutant malware is able to manipulate their syntax only. To this end, we first extract API calls from malware and convert to a hierarchical behavior graph that represents with identical 128 nodes based on the semantics. Later, we extract unique subgraphs from the hierarchical behavior graphs as semantic signatures representing common behaviors of a specific malware family. To evaluate BinGraph, we analyzed a total of 827 malware samples that consist of 10 malware families with 1,202 benign binaries. Among the malware, 20% samples randomly chosen from each malware family were used for extracting semantic signatures, and rest of them were used for assessing detection accuracy. Finally, only 32 subgraphs were selected as the semantic signatures. BinGraph discovered malware variants with 98% of detection accuracy.
AB - Malware landscape has been dramatically elevated over the last decade. The main reason of the increase is that new malware variants can be produced easily using simple code obfuscation techniques. Once the obfuscation is applied, the malware can change their syntactics while preserving semantics, and bypass anti-virus (AV) scanners. Malware authors, thus, commonly use the code obfuscation techniques to generate metamorphic malware. Nevertheless, signature based AV techniques are limited to detect the metamorphic malware since they are commonly based on the syntactic signature matching. In this paper, we propose BinGraph, a new mechanism that accurately discovers metamorphic malware. BinGraph leverages the semantics of malware, since the mutant malware is able to manipulate their syntax only. To this end, we first extract API calls from malware and convert to a hierarchical behavior graph that represents with identical 128 nodes based on the semantics. Later, we extract unique subgraphs from the hierarchical behavior graphs as semantic signatures representing common behaviors of a specific malware family. To evaluate BinGraph, we analyzed a total of 827 malware samples that consist of 10 malware families with 1,202 benign binaries. Among the malware, 20% samples randomly chosen from each malware family were used for extracting semantic signatures, and rest of them were used for assessing detection accuracy. Finally, only 32 subgraphs were selected as the semantic signatures. BinGraph discovered malware variants with 98% of detection accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84874575571&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874575571&partnerID=8YFLogxK
U2 - 10.1109/MALWARE.2012.6461015
DO - 10.1109/MALWARE.2012.6461015
M3 - Conference contribution
AN - SCOPUS:84874575571
SN - 9781467348782
SP - 104
EP - 111
BT - Proceedings of the 2012 7th International Conference on Malicious and Unwanted Software, Malware 2012
ER -