Name
Abstract | ||
|---|---|---|
•A novel evolving possibilistic Cauchy clustering (eCauchy) is presented that is able to learn a classifier in an online manner on a stream of data.•As oppose to some evolving algorithms the presented approach has only few tuning parameters.•The eCauchy clustering is tested on large-scale monitoring for cyber-attacks on a KDD data set.•The results are given for all three KDD data sets in a form of typical classifier goodness measures.•The obtained results are promising and show that the approach can be potentially useful for monitoring network traffic. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2017.11.008 | Applied Soft Computing |
Keywords | Field | DocType |
Big-data,Data stream,Evolving clustering,Cauchy density,Cyber security | Data mining,Data stream,Computer science,Raw data,Cauchy distribution,Artificial intelligence,Classifier (linguistics),Cluster analysis,Big data,Intrusion detection system,Machine learning,The Internet | Journal |
Volume | ISSN | Citations |
62 | 1568-4946 | 2 |
PageRank | References | Authors |
0.36 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor Skrjanc | 1 | 354 | 52.47 |
| Seiichi Ozawa | 2 | 229 | 33.89 |
| Tao Ban | 3 | 27 | 7.22 |
| Dejan Dovzan | 4 | 117 | 8.18 |