Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386361505> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4386361505 abstract "The rise of ransomware has emerged as a pressing concern for the technology industry, demanding prompt action to prevent monetary and ethical exploitation. Therefore, an accurate approach is imperative to identify and thwart such attacks effectively. Most of the prior ransomware detection techniques either are signature-based, which are inefficient to identify new ransomware, or utilize a dynamic analysis, which are complicated and computationally expensive. This paper proposes a feature selection-based framework along with different machine learning and deep learning algorithms that can effectively detect ransomware based on features extracted from the files. We performed various experiments beginning with filter, wrapper and embedded methods of feature selection and then applied Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Extreme Gradient Boost (XGB) and Multi-layer Perceptron (MLP) on a ransomware dataset that contains the features and label from files. The experimental results demonstrate that RF and MLP classifiers with ANOVA filter method of feature selection outperform other methods in terms of accuracy, precision, and recall." @default.
- W4386361505 created "2023-09-02" @default.
- W4386361505 creator A5045582103 @default.
- W4386361505 creator A5087505859 @default.
- W4386361505 date "2023-07-01" @default.
- W4386361505 modified "2023-09-27" @default.
- W4386361505 title "Ransomware Classification Using Machine Learning" @default.
- W4386361505 cites W2066832805 @default.
- W4386361505 cites W2892249614 @default.
- W4386361505 cites W2911731407 @default.
- W4386361505 cites W2947499363 @default.
- W4386361505 cites W2954539634 @default.
- W4386361505 cites W3004344696 @default.
- W4386361505 cites W3005124797 @default.
- W4386361505 cites W3034402928 @default.
- W4386361505 cites W3099702369 @default.
- W4386361505 cites W3126704266 @default.
- W4386361505 cites W3160751779 @default.
- W4386361505 cites W3186962463 @default.
- W4386361505 cites W4214850595 @default.
- W4386361505 cites W4224941017 @default.
- W4386361505 cites W4283325293 @default.
- W4386361505 cites W4316690373 @default.
- W4386361505 doi "https://doi.org/10.1109/icccn58024.2023.10230176" @default.
- W4386361505 hasPublicationYear "2023" @default.
- W4386361505 type Work @default.
- W4386361505 citedByCount "0" @default.
- W4386361505 crossrefType "proceedings-article" @default.
- W4386361505 hasAuthorship W4386361505A5045582103 @default.
- W4386361505 hasAuthorship W4386361505A5087505859 @default.
- W4386361505 hasConcept C106131492 @default.
- W4386361505 hasConcept C119857082 @default.
- W4386361505 hasConcept C12267149 @default.
- W4386361505 hasConcept C124101348 @default.
- W4386361505 hasConcept C138885662 @default.
- W4386361505 hasConcept C148483581 @default.
- W4386361505 hasConcept C153180895 @default.
- W4386361505 hasConcept C154945302 @default.
- W4386361505 hasConcept C169258074 @default.
- W4386361505 hasConcept C179717631 @default.
- W4386361505 hasConcept C2776401178 @default.
- W4386361505 hasConcept C2777667771 @default.
- W4386361505 hasConcept C31972630 @default.
- W4386361505 hasConcept C38652104 @default.
- W4386361505 hasConcept C41008148 @default.
- W4386361505 hasConcept C41895202 @default.
- W4386361505 hasConcept C50644808 @default.
- W4386361505 hasConcept C52001869 @default.
- W4386361505 hasConcept C52622490 @default.
- W4386361505 hasConcept C541664917 @default.
- W4386361505 hasConcept C60908668 @default.
- W4386361505 hasConcept C84525736 @default.
- W4386361505 hasConceptScore W4386361505C106131492 @default.
- W4386361505 hasConceptScore W4386361505C119857082 @default.
- W4386361505 hasConceptScore W4386361505C12267149 @default.
- W4386361505 hasConceptScore W4386361505C124101348 @default.
- W4386361505 hasConceptScore W4386361505C138885662 @default.
- W4386361505 hasConceptScore W4386361505C148483581 @default.
- W4386361505 hasConceptScore W4386361505C153180895 @default.
- W4386361505 hasConceptScore W4386361505C154945302 @default.
- W4386361505 hasConceptScore W4386361505C169258074 @default.
- W4386361505 hasConceptScore W4386361505C179717631 @default.
- W4386361505 hasConceptScore W4386361505C2776401178 @default.
- W4386361505 hasConceptScore W4386361505C2777667771 @default.
- W4386361505 hasConceptScore W4386361505C31972630 @default.
- W4386361505 hasConceptScore W4386361505C38652104 @default.
- W4386361505 hasConceptScore W4386361505C41008148 @default.
- W4386361505 hasConceptScore W4386361505C41895202 @default.
- W4386361505 hasConceptScore W4386361505C50644808 @default.
- W4386361505 hasConceptScore W4386361505C52001869 @default.
- W4386361505 hasConceptScore W4386361505C52622490 @default.
- W4386361505 hasConceptScore W4386361505C541664917 @default.
- W4386361505 hasConceptScore W4386361505C60908668 @default.
- W4386361505 hasConceptScore W4386361505C84525736 @default.
- W4386361505 hasLocation W43863615051 @default.
- W4386361505 hasOpenAccess W4386361505 @default.
- W4386361505 hasPrimaryLocation W43863615051 @default.
- W4386361505 hasRelatedWork W2984537336 @default.
- W4386361505 hasRelatedWork W2985924212 @default.
- W4386361505 hasRelatedWork W3034132578 @default.
- W4386361505 hasRelatedWork W3150651898 @default.
- W4386361505 hasRelatedWork W3168994312 @default.
- W4386361505 hasRelatedWork W3202148033 @default.
- W4386361505 hasRelatedWork W4362711840 @default.
- W4386361505 hasRelatedWork W4377964522 @default.
- W4386361505 hasRelatedWork W4384345534 @default.
- W4386361505 hasRelatedWork W2345184372 @default.
- W4386361505 isParatext "false" @default.
- W4386361505 isRetracted "false" @default.
- W4386361505 workType "article" @default.