Name
Abstract | ||
|---|---|---|
The evaluation of algorithms and techniques to implement intrusion detection systems heavily rely on the existence of well designed datasets. In the last years, a lot of efforts have been done towards building these datasets. Yet, there is still room to improve. In this paper, a comprehensive review of existing datasets is first done, making emphasis on their main shortcomings. Then, we present a new dataset that is built with real traffic and up-to-date attacks. The main advantage of this dataset over previous ones is its usefulness for evaluating IDSs that consider long-term evolution and traffic periodicity. Models that consider differences in daytime/night or weekdays/weekends can also be trained and evaluated with it. We discuss all the requirements for a modern IDS evaluation dataset and analyze how the one presented here meets the different needs. |
Year | Venue | Field |
|---|---|---|
2018 | Computers & Security | Data mining,Computer security,Computer science,Intrusion detection system |
DocType | Volume | Citations |
Journal | 73 | 1 |
PageRank | References | Authors |
0.36 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Maciá-Fernández | 1 | 616 | 33.77 |
| Jose A. Camacho | 2 | 58 | 7.98 |
| Roberto Magán-Carrión | 3 | 39 | 6.28 |
| Pedro García-Teodoro | 4 | 214 | 18.28 |
| Roberto Therón | 5 | 404 | 60.72 |