Name
Abstract | ||
|---|---|---|
Signature matching methods are inadequate to detect unseen malwares. In this paper an API (Application Programming Interface) based data mining method is proposed to detect unseen malwares. The data mining algorithm, objective-oriented associate mining (OOA), is employed to mine association rules for detecting malwares. To find association rules with strong discrimination power, an improved algorithm for frequent item generation is presented. In this algorithm a frequent item is evaluated by its support and its classification capability. The experiments prove that the proposed methods are effective and can be used to detect malware variants and unknown malicious executable. |
Year | Venue | Keywords |
|---|---|---|
2013 | PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 | Tracking,Malware detection,Objective-oriented associate mining,Security,Classification,Machine learning |
Field | DocType | ISSN |
Data mining,Data stream mining,Computer science,Association mining,Malware | Conference | 2160-133X |
ISBN | Citations | PageRank |
978-1-4799-0260-6 | 1 | 0.35 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Xiao | 1 | 14 | 1.41 |
| Yuxin Ding | 2 | 237 | 21.52 |
| Yibin Zhang | 3 | 29 | 4.70 |
| Tang Ke | 4 | 2798 | 139.09 |
| Dai Wei | 5 | 13 | 2.70 |