Name
Abstract | ||
|---|---|---|
Clustering is an important part of the malware analysis. The malware clustering algorithms commonly used at present have gradually can not adapt to the growing number of malware. In order to improve the malware clustering algorithm, this paper uses the clustering algorithm based on Shared Nearest Neighbor (SNN), and uses frequencies of the system calls as the features for input. This algorithm combined with the DBSCAN which is traditional density-based clustering algorithm in data mining. This makes it is a better application in the process of clustering of malware. The results of clusters demonstrate that the effect of the algorithm of clustering is good. And the algorithm is simple to implement and easy to complete automated analysis. It can be applied to actual automated analysis of malware. |
Year | Venue | Field |
|---|---|---|
2015 | ICCCS | k-nearest neighbors algorithm,Data mining,Computer science,Malware,Cluster analysis,DBSCAN,Malware analysis,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuwei Wang | 1 | 0 | 0.34 |
| Baosheng Wang | 2 | 3 | 5.81 |
| Tang Yong | 3 | 0 | 0.34 |
| Bo Yu | 4 | 85 | 23.58 |