Name
Abstract | ||
|---|---|---|
There are a lot of malicious codes on the internet and many research studies methods for detection of them. Generally, detection methods of malicious codes compare source codes through definition and analysis pattern of malicious codes. In this paper, proposed method is a malicious code detection using relations and concepts between codes pattern based on semantics. Also, this method is detection of malicious script code through token conceptualization for extraction of relations and concepts in source codes because conceptual graph and regularization pattern matching between malicious behaviors in codes. In experiment, we test a malicious behavior distinction based on SVM(Support Vector Machine) training and the result is indicated adequate rate of malicious code detection. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2401603.2401671 | RACS |
Keywords | Field | DocType |
malicious behavior,conceptual graph,detection method,regularization pattern,analysis pattern,source code,malicious script code,malicious code detection,codes pattern,unknown malicious script code,malicious behavior distinction,malicious code,security | Source code,Computer science,Support vector machine,Conceptual graph,Conceptualization,Theoretical computer science,Pattern matching,Security token,Semantics,The Internet,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hayoung Kim | 1 | 21 | 4.04 |
| Junho Choi | 2 | 366 | 60.87 |
| Dongjin Choi | 3 | 56 | 10.45 |
| Han-Suk Choi | 4 | 0 | 1.69 |
| Pankoo Kim | 5 | 0 | 0.34 |