Name
Abstract | ||
|---|---|---|
The number of malware has sharply increased over years, and it caused various damages on computing systems and data. In this paper, we propose techniques to detect malware variants. Malware authors usually reuse malware modules when they generate new malware or malware variants. Therefore, malware variants have common code for some functions in their binary files. We focused on this common code in this research, and proposed the techniques to detect malware variants through similarity calculation of user-defined function. Since many malware variants evade malware detection system by transforming their static signatures, to cope with this problem, we applied pattern matching algorithms for DNA variations in Bioinformatics to similarity calculation of malware binary files. Since the pattern matching algorithm we used provides the local alignment function, small modification of functions can be overcome. Experimental results show that our proposed method can detect malware similarity and it is more resilient than other methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2663761.2664222 | RACS |
Keywords | DocType | Citations |
invasive software,security,malware analysis,static analysis,smith-waterman algorithm,smith waterman algorithm | Conference | 2 |
PageRank | References | Authors |
0.40 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae-Guen Kim | 1 | 35 | 4.94 |
| Jung Bin Park | 2 | 2 | 0.40 |
| In Gyeom Cho | 3 | 2 | 0.40 |
| BooJoong Kang | 4 | 118 | 11.55 |
| Eul Gyu Im | 5 | 175 | 24.80 |
| SooYong Kang | 6 | 2 | 0.40 |