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230515 ||| eng |
| 020 |
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|a 9783036571744
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|a 9783036571751
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|a books978-3-0365-7174-4
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| 100 |
1 |
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|a Yerima, Suleiman
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| 245 |
0 |
0 |
|a High Accuracy Detection of Mobile Malware Using Machine Learning
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (226 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a android botnets
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| 653 |
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|a botnet detection
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| 653 |
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|a CNN-GRU
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| 653 |
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|a image processing
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| 653 |
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|a code vulnerability
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| 653 |
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|a n/a
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| 653 |
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|a malware analysis and detection
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| 653 |
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|a polyglots
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| 653 |
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|a deep learning
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| 653 |
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|a gated recurrent unit
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| 653 |
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|a neural networks
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| 653 |
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|a email phishing
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| 653 |
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|a systematic literature review
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| 653 |
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|a steganalysis
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| 653 |
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|a phishing detection
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| 653 |
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|a salp swarm algorithm
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| 653 |
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|a machine learning (ML)
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| 653 |
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|a mobile security
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| 653 |
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|a ensemble classification
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| 653 |
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|a CNN-LSTM
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| 653 |
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|a reinforcement learning
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| 653 |
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|a Information technology industries / bicssc
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| 653 |
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|a static analysis
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| 653 |
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|a connection weights
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| 653 |
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|a adversarial sample
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| 653 |
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|a recurrent neural networks
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| 653 |
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|a dense neural networks
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| 653 |
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|a security
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| 653 |
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|a convolutional neural networks
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| 653 |
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|a Monte-Carlo simulation
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| 653 |
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|a Histogram of Oriented Gradients
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| 653 |
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|a applied machine learning
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| 653 |
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|a Android security
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| 653 |
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|a malware detection
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| 653 |
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|a long short-term memory
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| 653 |
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|a hybrid analysis
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| 653 |
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|a multilayer perceptron
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| 653 |
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|a digital forensic
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| 653 |
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|a Android botnets
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| 653 |
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|a dynamic analysis
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| 653 |
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|a malware
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| 653 |
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|a convolutional neural network
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| 653 |
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|a optimization
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| 653 |
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|a business email compromise (BEC)
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| 653 |
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|a steganography
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| 653 |
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|a neural network
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| 700 |
1 |
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|a Yerima, Suleiman
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-0365-7174-4
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/7088
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/99995
|z DOAB: description of the publication
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| 082 |
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|a 800
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|a 000
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|a 600
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|a 330
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|a As 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.
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