Title
Feature Set Selection In Data Mining Techniques For Unknown Virus Detection - A Comparison Study
Keywords
Data mining; Feature selection; Feature set; Virus detection
Abstract
Detecting unknown viruses is a challenging research topic. Data mining approaches have been used to detect unknown viruses. The key to data mining lies on the feature set to be used. There are several different approaches have been tried before, simple heuristics, static features and dynamic features. In this paper, we present several different data mining approaches and compare the result of these approaches. Copyright © 2009 ACM.
Publication Date
11-9-2009
Publication Title
ACM International Conference Proceeding Series
Number of Pages
-
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1145/1558607.1558672
Copyright Status
Unknown
Socpus ID
70350643652 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/70350643652
STARS Citation
Dai, Jianyong; Guha, Ratan; and Lee, Joohan, "Feature Set Selection In Data Mining Techniques For Unknown Virus Detection - A Comparison Study" (2009). Scopus Export 2000s. 11539.
https://stars.library.ucf.edu/scopus2000/11539