Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367053352> ?p ?o ?g. }
- W4367053352 abstract "Summary With the rapid growth of malicious codes, personal privacy, and Internet security are seriously threatened. Existing transfer learning‐based malicious code detection improves detection accuracy by transferring pre‐trained neural networks. However, it cannot efficiently tune the structure and parameters of the neural networks. Here, we first propose a novel many‐objective transfer model. It mainly focuses on the detection accuracy and the total number of parameters of the neural network model. The optimal structure and parameters are captured from the pre‐trained neural network by many‐objective optimization algorithm. Second, the partitioned crossover‐mutation vector angle‐based evolutionary algorithm for unconstrained many‐objective optimization is proposed to solve the model. The algorithm performs crossover mutation operations in different ways on different regions of the candidate solution to improve population diversity. The simulation results show that the model can reduce the pre‐trained neural network structure by 49% while maintaining the accuracy in malicious code detection." @default.
- W4367053352 created "2023-04-27" @default.
- W4367053352 creator A5010039524 @default.
- W4367053352 creator A5019349014 @default.
- W4367053352 creator A5052719394 @default.
- W4367053352 creator A5053085908 @default.
- W4367053352 creator A5062712066 @default.
- W4367053352 date "2023-04-25" @default.
- W4367053352 modified "2023-10-14" @default.
- W4367053352 title "Malicious code detection based on many‐objective transfer model" @default.
- W4367053352 cites W1824976302 @default.
- W4367053352 cites W1893133781 @default.
- W4367053352 cites W1996975221 @default.
- W4367053352 cites W2010065958 @default.
- W4367053352 cites W2022485595 @default.
- W4367053352 cites W2040255266 @default.
- W4367053352 cites W2040622444 @default.
- W4367053352 cites W2091088253 @default.
- W4367053352 cites W2108968575 @default.
- W4367053352 cites W2155579778 @default.
- W4367053352 cites W2177448797 @default.
- W4367053352 cites W2214409633 @default.
- W4367053352 cites W2253711217 @default.
- W4367053352 cites W2326149522 @default.
- W4367053352 cites W2469950730 @default.
- W4367053352 cites W2607620859 @default.
- W4367053352 cites W2883822334 @default.
- W4367053352 cites W2920102261 @default.
- W4367053352 cites W2950754826 @default.
- W4367053352 cites W2963557007 @default.
- W4367053352 cites W2978348882 @default.
- W4367053352 cites W2981285812 @default.
- W4367053352 cites W3000455992 @default.
- W4367053352 cites W3004280948 @default.
- W4367053352 cites W3006076509 @default.
- W4367053352 cites W3006495767 @default.
- W4367053352 cites W3008497156 @default.
- W4367053352 cites W3108259217 @default.
- W4367053352 cites W3119972824 @default.
- W4367053352 cites W3128183952 @default.
- W4367053352 cites W3128190425 @default.
- W4367053352 cites W3129906305 @default.
- W4367053352 cites W3171579381 @default.
- W4367053352 cites W3174909610 @default.
- W4367053352 cites W3200960983 @default.
- W4367053352 cites W3203731077 @default.
- W4367053352 cites W3209034791 @default.
- W4367053352 cites W4200256722 @default.
- W4367053352 cites W4200629745 @default.
- W4367053352 cites W4205974944 @default.
- W4367053352 cites W4206963665 @default.
- W4367053352 cites W4282032687 @default.
- W4367053352 cites W4283747681 @default.
- W4367053352 cites W4289821535 @default.
- W4367053352 cites W4292062030 @default.
- W4367053352 cites W4312815804 @default.
- W4367053352 cites W4316022169 @default.
- W4367053352 doi "https://doi.org/10.1002/cpe.7728" @default.
- W4367053352 hasPublicationYear "2023" @default.
- W4367053352 type Work @default.
- W4367053352 citedByCount "0" @default.
- W4367053352 crossrefType "journal-article" @default.
- W4367053352 hasAuthorship W4367053352A5010039524 @default.
- W4367053352 hasAuthorship W4367053352A5019349014 @default.
- W4367053352 hasAuthorship W4367053352A5052719394 @default.
- W4367053352 hasAuthorship W4367053352A5053085908 @default.
- W4367053352 hasAuthorship W4367053352A5062712066 @default.
- W4367053352 hasConcept C104317684 @default.
- W4367053352 hasConcept C119857082 @default.
- W4367053352 hasConcept C122507166 @default.
- W4367053352 hasConcept C124101348 @default.
- W4367053352 hasConcept C144024400 @default.
- W4367053352 hasConcept C149923435 @default.
- W4367053352 hasConcept C150899416 @default.
- W4367053352 hasConcept C154945302 @default.
- W4367053352 hasConcept C173608175 @default.
- W4367053352 hasConcept C177264268 @default.
- W4367053352 hasConcept C185592680 @default.
- W4367053352 hasConcept C199360897 @default.
- W4367053352 hasConcept C2776175482 @default.
- W4367053352 hasConcept C2776760102 @default.
- W4367053352 hasConcept C2908647359 @default.
- W4367053352 hasConcept C41008148 @default.
- W4367053352 hasConcept C501734568 @default.
- W4367053352 hasConcept C50644808 @default.
- W4367053352 hasConcept C55493867 @default.
- W4367053352 hasConceptScore W4367053352C104317684 @default.
- W4367053352 hasConceptScore W4367053352C119857082 @default.
- W4367053352 hasConceptScore W4367053352C122507166 @default.
- W4367053352 hasConceptScore W4367053352C124101348 @default.
- W4367053352 hasConceptScore W4367053352C144024400 @default.
- W4367053352 hasConceptScore W4367053352C149923435 @default.
- W4367053352 hasConceptScore W4367053352C150899416 @default.
- W4367053352 hasConceptScore W4367053352C154945302 @default.
- W4367053352 hasConceptScore W4367053352C173608175 @default.
- W4367053352 hasConceptScore W4367053352C177264268 @default.
- W4367053352 hasConceptScore W4367053352C185592680 @default.
- W4367053352 hasConceptScore W4367053352C199360897 @default.
- W4367053352 hasConceptScore W4367053352C2776175482 @default.
- W4367053352 hasConceptScore W4367053352C2776760102 @default.