# Testing a rich sample of cybercrimes dataset  by using powerful classifiers’ competences

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/testing-a-rich-sample-of-cybercrimes-dataset-by-using-powerful-classifiers/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Elrasheed Ismail Mohommoud Zayid,Abdulmalik A. HUMAYED,Yagoub Abbker Adam |
| Citations | 2 |
| DOI | 10.36227/techrxiv.170491687.71856965/v1 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.170491687.71856965 |
| OpenAlex ID | https://openalex.org/W4390691452 |
| Type | preprint |
| Year | 2024 |

## Paper authors

- [Yagoub Abbker Adam](https://scholariq.org/researchers/yagoub-abbker-adam/)

## Paper primary topic

- [Imbalanced Data Classification Techniques](https://scholariq.org/topics/imbalanced-data-classification-techniques/)

## Paper topics

- [Imbalanced Data Classification Techniques](https://scholariq.org/topics/imbalanced-data-classification-techniques/)
- [Network Security and Intrusion Detection](https://scholariq.org/topics/network-security-and-intrusion-detection/)
- [Cybercrime and Law Enforcement Studies](https://scholariq.org/topics/cybercrime-and-law-enforcement-studies/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.