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This dataset is a transformation of the CICDDOS2019 collection of datasets by Iman Sharafaldin et al. (2019). All datasets have been combined and have had their labels standardised. Infinity values have been removed. A supplement of Benign tuples has been introduced from CICIDS2017, another collection of datasets by Iman Sharafaldin et al. (2018), to increase the proportion of Benign tuples within the dataset. Our paper: source1, source 2 (preprint).
{"references": ["Sharafaldin, I., Lashkari, A.H., Hakak, S. and Ghorbani, A.A., 2019, October. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST) (pp. 1-8). IEEE.", "Sharafaldin, I., Lashkari, A.H. and Ghorbani, A.A., 2018. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1, pp.108-116."]}
| 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 | 90 | |
| downloads | 6 |

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