Publication: Robust Malware Family Classification Using Effective Features and Classifiers
| dc.citedby | 4 | |
| dc.contributor.author | Hammad B.T. | en_US |
| dc.contributor.author | Jamil N. | en_US |
| dc.contributor.author | Ahmed I.T. | en_US |
| dc.contributor.author | Zain Z.M. | en_US |
| dc.contributor.author | Basheer S. | en_US |
| dc.contributor.authorid | 57193327622 | en_US |
| dc.contributor.authorid | 36682671900 | en_US |
| dc.contributor.authorid | 57193324906 | en_US |
| dc.contributor.authorid | 36900229100 | en_US |
| dc.contributor.authorid | 57207113102 | en_US |
| dc.date.accessioned | 2023-05-29T09:36:48Z | |
| dc.date.available | 2023-05-29T09:36:48Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research�s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model�s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method�s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent. � 2022 by the authors. | en_US |
| dc.description.nature | Final | en_US |
| dc.identifier.ArtNo | 7877 | |
| dc.identifier.doi | 10.3390/app12157877 | |
| dc.identifier.issue | 15 | |
| dc.identifier.scopus | 2-s2.0-85136983491 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136983491&doi=10.3390%2fapp12157877&partnerID=40&md5=277f2d2c809d1e89203c985d4a17c97e | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/26800 | |
| dc.identifier.volume | 12 | |
| dc.publisher | MDPI | en_US |
| dc.relation.ispartof | All Open Access, Gold | |
| dc.source | Scopus | |
| dc.sourcetitle | Applied Sciences (Switzerland) | |
| dc.title | Robust Malware Family Classification Using Effective Features and Classifiers | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication |