Publication: Malware Detection Using Deep Learning and Correlation-Based Feature Selection
| dc.citedby | 70 | |
| dc.contributor.author | Alomari E.S. | en_US |
| dc.contributor.author | Nuiaa R.R. | en_US |
| dc.contributor.author | Alyasseri Z.A.A. | en_US |
| dc.contributor.author | Mohammed H.J. | en_US |
| dc.contributor.author | Sani N.S. | en_US |
| dc.contributor.author | Esa M.I. | en_US |
| dc.contributor.author | Musawi B.A. | en_US |
| dc.contributor.authorid | 58668473000 | en_US |
| dc.contributor.authorid | 57226309117 | en_US |
| dc.contributor.authorid | 57862594800 | en_US |
| dc.contributor.authorid | 57202657688 | en_US |
| dc.contributor.authorid | 57196190931 | en_US |
| dc.contributor.authorid | 57203682775 | en_US |
| dc.contributor.authorid | 57439487000 | en_US |
| dc.date.accessioned | 2024-10-14T03:21:51Z | |
| dc.date.available | 2024-10-14T03:21:51Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively. � 2023 by the authors. | en_US |
| dc.description.nature | Final | en_US |
| dc.identifier.ArtNo | 123 | |
| dc.identifier.doi | 10.3390/sym15010123 | |
| dc.identifier.issue | 1 | |
| dc.identifier.scopus | 2-s2.0-85146783397 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146783397&doi=10.3390%2fsym15010123&partnerID=40&md5=f2337f655af4b8f4a26c0b963638681a | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/34699 | |
| dc.identifier.volume | 15 | |
| dc.publisher | MDPI | en_US |
| dc.relation.ispartof | All Open Access | |
| dc.relation.ispartof | Gold Open Access | |
| dc.source | Scopus | |
| dc.sourcetitle | Symmetry | |
| dc.subject | deep learning | |
| dc.subject | dense model | |
| dc.subject | feature selection | |
| dc.subject | LSTM | |
| dc.subject | malware detection | |
| dc.title | Malware Detection Using Deep Learning and Correlation-Based Feature Selection | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication |