Name
Title | ||
|---|---|---|
Using Feature Selection to Improve Performance of Three-Tier Intrusion Detection System. |
Abstract | ||
|---|---|---|
Social media services have become an essential part of daily life. Once 5G services launch in the near future, the annual network IP flow can be expected to increase significantly. In case of security threats, network attacks will become more various and harder to detect. The intrusion detection system (IDS) in the network defense system is in charge of detecting malicious activities online. The research proposed an intelligent three-tier IDS that can process high-speed network flow and classify attack behaviors into nine kinds of attacks by seven machine learning methods. Based on the operation time, the detection process can be divided into the offline phase, which trains models by machine learning, and the online phase, which enhances the detection rate of network attacks by a three-tier filtering process. In the experiment, UNSW-NB15 was adopted as the dataset, where the accuracy of intrusion detection approached 98%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-15035-8_75 | AINA Workshops |
Field | DocType | Citations |
Flow network,Social media,Feature selection,Computer science,Computer network,Filter (signal processing),Network defense,Train,Intrusion detection system | Conference | 0 |
PageRank | References | Authors |
0.34 | 13 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Jen Su | 1 | 0 | 1.35 |
| Pei-Yu Huang | 2 | 0 | 0.68 |
| Wu-Chih Hu | 3 | 244 | 27.01 |
| Hsuan-Yu Lin | 4 | 0 | 0.34 |
| Chen-Yu Kao | 5 | 0 | 0.34 |
| Shan-Hsiung Hsieh | 6 | 0 | 0.34 |
| Chun-Li Lin | 7 | 0 | 0.34 |