Name
Abstract | ||
|---|---|---|
Intrusion detection systems (IDSs) play an important role to defend networks from cyber attacks. Among them, anomaly-based IDSs can detect unknown attacks like 0-day attacks that are hard to detect by using signature-based system. However, they have problems that their performance depends on a learning dataset. It is very hard to prepare an appropriate learning dataset in a static fashion, because the traffic in the Internet changes quite dynamically and complexity. In this paper, we propose a method that follows traffic trend by combining multiple classifiers. We evaluate our method using Kyoto2006+ and existing algorithm. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/SAINT.2011.70 | SAINT |
Keywords | Field | DocType |
internet change,existing algorithm,traffic trend,important role,appropriate learning dataset,anomaly-based ids,cyber attack,improving performance,multiple classifier,anomaly-based idss,0-day attack,combining multiple classifiers,intrusion detection system,cyber attacks,intrusion detection systems,clustering,servers,clustering algorithms,anomaly based ids,false positive rate,testing,internet,feature extraction,computer network security | Data mining,Anomaly detection,False positive rate,Computer science,Network security,Intrusion prevention system,Anomaly-based intrusion detection system,Artificial intelligence,Cluster analysis,Intrusion detection system,Machine learning,The Internet | Conference |
Citations | PageRank | References |
5 | 0.78 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuya Kishimoto | 1 | 8 | 1.35 |
| Hirofumi Yamaki | 2 | 81 | 21.02 |
| Hiroki Takakura | 3 | 244 | 58.93 |