Matches in SemOpenAlex for { <https://semopenalex.org/work/W3130705540> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W3130705540 endingPage "519" @default.
- W3130705540 startingPage "519" @default.
- W3130705540 abstract "Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers." @default.
- W3130705540 created "2021-03-01" @default.
- W3130705540 creator A5049377745 @default.
- W3130705540 creator A5072809002 @default.
- W3130705540 creator A5076877194 @default.
- W3130705540 creator A5086501042 @default.
- W3130705540 date "2021-02-23" @default.
- W3130705540 modified "2023-09-25" @default.
- W3130705540 title "Deep Learning Techniques for Android Botnet Detection" @default.
- W3130705540 cites W2064675550 @default.
- W3130705540 cites W2112796928 @default.
- W3130705540 cites W2117189826 @default.
- W3130705540 cites W2495632520 @default.
- W3130705540 cites W2791505513 @default.
- W3130705540 cites W2919115771 @default.
- W3130705540 cites W2944720157 @default.
- W3130705540 cites W2981185325 @default.
- W3130705540 doi "https://doi.org/10.3390/electronics10040519" @default.
- W3130705540 hasPublicationYear "2021" @default.
- W3130705540 type Work @default.
- W3130705540 sameAs 3130705540 @default.
- W3130705540 citedByCount "26" @default.
- W3130705540 countsByYear W31307055402021 @default.
- W3130705540 countsByYear W31307055402022 @default.
- W3130705540 countsByYear W31307055402023 @default.
- W3130705540 crossrefType "journal-article" @default.
- W3130705540 hasAuthorship W3130705540A5049377745 @default.
- W3130705540 hasAuthorship W3130705540A5072809002 @default.
- W3130705540 hasAuthorship W3130705540A5076877194 @default.
- W3130705540 hasAuthorship W3130705540A5086501042 @default.
- W3130705540 hasBestOaLocation W31307055401 @default.
- W3130705540 hasConcept C108583219 @default.
- W3130705540 hasConcept C110875604 @default.
- W3130705540 hasConcept C111919701 @default.
- W3130705540 hasConcept C119857082 @default.
- W3130705540 hasConcept C154945302 @default.
- W3130705540 hasConcept C22735295 @default.
- W3130705540 hasConcept C2989133298 @default.
- W3130705540 hasConcept C38652104 @default.
- W3130705540 hasConcept C41008148 @default.
- W3130705540 hasConcept C541664917 @default.
- W3130705540 hasConcept C557433098 @default.
- W3130705540 hasConceptScore W3130705540C108583219 @default.
- W3130705540 hasConceptScore W3130705540C110875604 @default.
- W3130705540 hasConceptScore W3130705540C111919701 @default.
- W3130705540 hasConceptScore W3130705540C119857082 @default.
- W3130705540 hasConceptScore W3130705540C154945302 @default.
- W3130705540 hasConceptScore W3130705540C22735295 @default.
- W3130705540 hasConceptScore W3130705540C2989133298 @default.
- W3130705540 hasConceptScore W3130705540C38652104 @default.
- W3130705540 hasConceptScore W3130705540C41008148 @default.
- W3130705540 hasConceptScore W3130705540C541664917 @default.
- W3130705540 hasConceptScore W3130705540C557433098 @default.
- W3130705540 hasIssue "4" @default.
- W3130705540 hasLocation W31307055401 @default.
- W3130705540 hasOpenAccess W3130705540 @default.
- W3130705540 hasPrimaryLocation W31307055401 @default.
- W3130705540 hasRelatedWork W1500042522 @default.
- W3130705540 hasRelatedWork W2032112873 @default.
- W3130705540 hasRelatedWork W2612928550 @default.
- W3130705540 hasRelatedWork W2945522736 @default.
- W3130705540 hasRelatedWork W2981185325 @default.
- W3130705540 hasRelatedWork W3130705540 @default.
- W3130705540 hasRelatedWork W3211751213 @default.
- W3130705540 hasRelatedWork W4234381785 @default.
- W3130705540 hasRelatedWork W4316087074 @default.
- W3130705540 hasRelatedWork W4379517951 @default.
- W3130705540 hasVolume "10" @default.
- W3130705540 isParatext "false" @default.
- W3130705540 isRetracted "false" @default.
- W3130705540 magId "3130705540" @default.
- W3130705540 workType "article" @default.