Downloads provided by UsageCounts
The datasets were preprocessed. Correlated features were removed. Related papers: [1] Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018 [2] Nour Moustafa, October 16, 2019, "UNSW_NB15 dataset", IEEE Dataport, doi: https://dx.doi.org/10.21227/8vf7-s525. [3] “Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746” [4] A. D. Kent, “Comprehensive, Multi-Source Cybersecurity Events,” Los Alamos National Laboratory, http://dx.doi.org/10.17021/1179829, 2015.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 95 | |
| downloads | 158 |

Views provided by UsageCounts
Downloads provided by UsageCounts