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- W4289229308 abstract "Data security is one of the most difficult concerns that businesses and associations face. In recent years, the regularity and severity of cybercrime have increased, with new methods for stealing, altering, and destroying information, as well as deactivating information systems, appearing daily. This paper evaluates the performance of some classification techniques on a malware dataset. Machine learning is a type of data analytics that allows computers to perform specific tasks without being given explicit instructions. Algorithms of ML can ‘reason’ the properties of before unseen samples. In malware detection, an unseen before the sample could be a new file. As of late, ML abilities have been utilized to plan both static and dynamic malware identification. The process has mainly two stages: firstly, features are extracted from the feature vector, and then, a suitable classification method is then applied for the result. Our paper aims to discuss the far more accurate feature abstraction and supervised classification techniques available. Precisely, random forest, decision trees, support vector machines, gradient boosting, and k-nearest neighbors classifiers were estimated. We proceeded with the experiment in two ways. Both results show that the methodologies can gain more than 95% accuracy." @default.
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- W4289229308 date "2022-01-01" @default.
- W4289229308 modified "2023-09-30" @default.
- W4289229308 title "Comparative Performance Evaluation of Supervised Classification Models on Large Static Malware Dataset" @default.
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- W4289229308 doi "https://doi.org/10.1007/978-981-19-2347-0_70" @default.
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