Name
Abstract | ||
|---|---|---|
With insider attacks becoming more common and costing organizations more every year, it has never been more crucial to be able to predict when an insider attack may happen. Network Anomaly Detection Systems (NADS) have the ability to identify unusual behavior making them useful in predicting cyberattacks, but often suffer from high false positive rates. Honeypots used in conjunction with NADS can help with learning attack behaviors and enable better prediction. However both honeypots and legacy NADS are generally deployed at the gateway to a network. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.eswa.2022.117073 | Expert Systems with Applications |
Keywords | DocType | Volume |
Network anomaly detection,Honeypots,Extreme value theory,False positives,Cyber security,Time series | Journal | 201 |
ISSN | Citations | PageRank |
0957-4174 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sevvandi Kandanaarachchi | 1 | 1 | 0.35 |
| Hideya Ochiai | 2 | 3 | 3.13 |
| Asha Rao | 3 | 1 | 0.35 |