Name
Abstract | ||
|---|---|---|
Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2089125.2089126 | ACM Comput. Surv. |
Keywords | Field | DocType |
analysis program,anti-virus vendor,new sample,dynamic analysis,malicious sample,new unknown threat,malicious threat,automated dynamic malware-analysis technique,human analyst,appropriate manner,unknown malicious behavior,malware | Computer security,Computer science,Malware,Malware analysis | Journal |
Volume | Issue | ISSN |
44 | 2 | 0360-0300 |
Citations | PageRank | References |
205 | 6.55 | 51 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Egele | 1 | 1613 | 102.07 |
| Theodoor Scholte | 2 | 262 | 10.67 |
| Engin Kirda | 3 | 5386 | 334.12 |
| Christopher Kruegel | 4 | 8799 | 516.05 |