# An Efficient DenseNet-Based Deep Learning Model for Malware Detection

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/an-efficient-densenet-based-deep-learning-model-for-malware-detection/

## Facts

| Field | Value |
| --- | --- |
| Author Names | J. Hemalatha,S. Abijah Roseline,S. Geetha,Seifedine Kadry,Robertas Damaševičius |
| Citations | 248 |
| DOI | 10.3390/e23030344 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/1099-4300/23/3/344/pdf?version=1616571736 |
| OpenAlex ID | https://openalex.org/W3138102940 |
| PMID | 33804035 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Seifedine Kadry](https://scholariq.org/researchers/seifedine-kadry/)

## Paper primary topic

- [Advanced Malware Detection Techniques](https://scholariq.org/topics/advanced-malware-detection-techniques/)

## Paper topics

- [Advanced Malware Detection Techniques](https://scholariq.org/topics/advanced-malware-detection-techniques/)
- [Network Security and Intrusion Detection](https://scholariq.org/topics/network-security-and-intrusion-detection/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

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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.