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dc.contributor.editorYerima, Suleiman
dc.date.accessioned2023-05-11T17:15:50Z
dc.date.available2023-05-11T17:15:50Z
dc.date.issued2023
dc.identifierONIX_20230511_9783036571751_12
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/99995
dc.description.abstractAs increasingly sophisticated and evasive malware attacks continue to emerge, more effective detection solutions to tackle the problem are being sought through the application of advanced machine learning techniques. This reprint presents several advances in the field including: a new method of generating adversarial samples through byte sequence feature extraction using deep learning; a state-of-the-art comparative evaluation of deep learning approaches for mobile botnet detection; a novel visualization-based approach that utilizes images for Android botnet detection; a study on the detection of drive-by exploits in images using deep learning; etc. Furthermore, this reprint presents state-of-the-art reviews about machine learning-based detection techniques that will increase researchers' knowledge in the field and enable them to identify future research and development directions.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industriesen_US
dc.subject.othermalware analysis and detection
dc.subject.otherapplied machine learning
dc.subject.othermobile security
dc.subject.otherneural network
dc.subject.otherensemble classification
dc.subject.otherbotnet detection
dc.subject.otherdeep learning
dc.subject.otherAndroid botnets
dc.subject.otherconvolutional neural networks
dc.subject.otherdense neural networks
dc.subject.otherrecurrent neural networks
dc.subject.otherlong short-term memory
dc.subject.othergated recurrent unit
dc.subject.otherCNN-LSTM
dc.subject.otherCNN-GRU
dc.subject.otherAndroid security
dc.subject.othermalware detection
dc.subject.othercode vulnerability
dc.subject.othermachine learning
dc.subject.othermalware
dc.subject.otherstatic analysis
dc.subject.otherdynamic analysis
dc.subject.otherhybrid analysis
dc.subject.othersecurity
dc.subject.otherMonte-Carlo simulation
dc.subject.otherreinforcement learning
dc.subject.otheradversarial sample
dc.subject.otherconvolutional neural network
dc.subject.otherHistogram of Oriented Gradients
dc.subject.otherimage processing
dc.subject.otherandroid botnets
dc.subject.otherdigital forensic
dc.subject.otheroptimization
dc.subject.othermultilayer perceptron
dc.subject.othersalp swarm algorithm
dc.subject.otherconnection weights
dc.subject.otherbusiness email compromise (BEC)
dc.subject.otheremail phishing
dc.subject.otherphishing detection
dc.subject.othermachine learning (ML)
dc.subject.othersystematic literature review
dc.subject.othersteganography
dc.subject.othersteganalysis
dc.subject.otherpolyglots
dc.subject.otherneural networks
dc.subject.othern/a
dc.titleHigh Accuracy Detection of Mobile Malware Using Machine Learning
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-7174-4
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036571751
oapen.relation.isbn9783036571744
oapen.pages226
oapen.place.publicationBasel


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