Name
Title | ||
|---|---|---|
Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation |
Abstract | ||
|---|---|---|
In this paper, Network-based anomaly intrusion detection method using Bayesian Networks was estimated probability values of behavior contexts based on Bayes theory and Indirect relation. The contexts of network-based FTP service was represented Bayesian Networks of graphic types. We profiled concisely network-based FTP behaviors using behavior context by prior, posterior and Indirect relation. And this method be able to visualize behavior profile to detect/analyze anomaly behavior. We achieve simulation to translate audit data of network into Bayesian network which is network-based behavior profile for anomaly detection. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74827-4_18 | KES (2) |
Keywords | Field | DocType |
anomaly detection,indirect relation,bayesian network,behavior profile,bayesian networks,network-based anomaly intrusion detection,bayes theory,behavior context,anomaly behavior,network-based behavior profile | Data mining,Anomaly detection,File Transfer Protocol,Computer science,Network packet,Anomaly-based intrusion detection system,Bayesian network,Artificial intelligence,Intrusion detection system,Machine learning,Bayes' theorem | Conference |
Volume | ISSN | Citations |
4693 | 0302-9743 | 6 |
PageRank | References | Authors |
0.57 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ByungRae Cha | 1 | 51 | 14.59 |
| Dongseob Lee | 2 | 8 | 1.10 |