Name
Abstract | ||
|---|---|---|
N-gram analysis is an approach that investigates the structure of a program using bytes, characters, or text strings. A key issue with N-gram analysis is feature selection amidst the explosion of features that occurs when N is increased. The experiments within this paper represent programs as operational code (opcode) density histograms gained through dynamic analysis. A support vector machine is used to create a reference model, which is used to evaluate two methods of feature reduction, which are “area of intersect” and “subspace analysis using eigenvectors.” The findings show that the relationships between features are complex and simple statistics filtering approaches do not provide a viable approach. However, eigenvector subspace analysis produces a suitable filter. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIFS.2013.2242890 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
materials,obfuscation,reference model,dynamic analysis,kernel,statistical analysis,knn,filtering,polymorphism,eigenvectors,support vector machine,support vector machines,feature selection,malware,svm | Data mining,Histogram,Opcode,Pattern recognition,Feature selection,Subspace topology,Reference model,Computer science,Support vector machine,Filter (signal processing),Artificial intelligence,Malware | Journal |
Volume | Issue | ISSN |
8 | 3 | 1556-6013 |
Citations | PageRank | References |
18 | 0.72 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philip O'Kane | 1 | 29 | 4.00 |
| Sakir Sezer | 2 | 1010 | 84.22 |
| Kieran McLaughlin | 3 | 208 | 22.19 |
| Eul Gyu Im | 4 | 175 | 24.80 |