Name
Title | ||
|---|---|---|
Anomaly-based network IDS false alarm filter using cluster-based alarm classification approach |
Abstract | ||
|---|---|---|
AbstractAnomaly-based network intrusion detection systems A-NIDS are an important and essential defence mechanism against network attacks. However, they generate a high volume of alarms that can be mixed with false-positive alarms, which poses a major challenge for these systems. Large amounts of false alarms prevent correct detection and make an immediate response impossible for intrusion detection system IDS. To mitigate this issue, this paper presents a strategy for filtering these alarms to reduce the rate of false-positive alarms of A-NIDS. This paper presents a new semi-supervised alarm classification method that does not require predefined knowledge of attack signatures or security personal feedback. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1504/IJSN.2017.081056 | Periodicals |
Field | DocType | Volume |
Data mining,False alarm,ALARM,Computer science,Computer security,Alarm management,Network security,Filter (signal processing),Anomaly-based intrusion detection system,Cluster analysis,Intrusion detection system | Journal | 12 |
Issue | ISSN | Citations |
1 | 1747-8405 | 1 |
PageRank | References | Authors |
0.34 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qais Qassim | 1 | 22 | 2.18 |
| Abdullah Mohd Zin | 2 | 28 | 12.07 |
| Mohd Juzaiddin Ab | 3 | 71 | 9.26 |