High Accuracy Detection of Mobile Malware Using Machine Learning
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 gene...
Na minha lista:
| Formato: | Online |
|---|---|
| Idioma: | inglês |
| Publicado em: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
|
| Assuntos: | |
| Acesso em linha: | ONIX_20230511_9783036571751_12 |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
| _version_ | 1869518145753972736 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-99995 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-999952024-03-30T12:51:05Z High Accuracy Detection of Mobile Malware Using Machine Learning Yerima, Suleiman malware analysis and detection applied machine learning mobile security neural network ensemble classification botnet detection deep learning Android botnets convolutional neural networks dense neural networks recurrent neural networks long short-term memory gated recurrent unit CNN-LSTM CNN-GRU Android security malware detection code vulnerability machine learning malware static analysis dynamic analysis hybrid analysis security Monte-Carlo simulation reinforcement learning adversarial sample convolutional neural network Histogram of Oriented Gradients image processing android botnets digital forensic optimization multilayer perceptron salp swarm algorithm connection weights business email compromise (BEC) email phishing phishing detection machine learning (ML) systematic literature review steganography steganalysis polyglots neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries 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. 2023-05-11T17:15:50Z 2023-05-11T17:15:50Z 2023 book ONIX_20230511_9783036571751_12 9783036571751 9783036571744 https://directory.doabooks.org/handle/20.500.12854/99995 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7088 https://mdpi.com/books/pdfview/book/7088 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7174-4 10.3390/books978-3-0365-7174-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036571751 9783036571744 226 Basel open access |
| spellingShingle | malware analysis and detection applied machine learning mobile security neural network ensemble classification botnet detection deep learning Android botnets convolutional neural networks dense neural networks recurrent neural networks long short-term memory gated recurrent unit CNN-LSTM CNN-GRU Android security malware detection code vulnerability machine learning malware static analysis dynamic analysis hybrid analysis security Monte-Carlo simulation reinforcement learning adversarial sample convolutional neural network Histogram of Oriented Gradients image processing android botnets digital forensic optimization multilayer perceptron salp swarm algorithm connection weights business email compromise (BEC) email phishing phishing detection machine learning (ML) systematic literature review steganography steganalysis polyglots neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries High Accuracy Detection of Mobile Malware Using Machine Learning |
| title | High Accuracy Detection of Mobile Malware Using Machine Learning |
| title_full | High Accuracy Detection of Mobile Malware Using Machine Learning |
| title_fullStr | High Accuracy Detection of Mobile Malware Using Machine Learning |
| title_full_unstemmed | High Accuracy Detection of Mobile Malware Using Machine Learning |
| title_short | High Accuracy Detection of Mobile Malware Using Machine Learning |
| title_sort | high accuracy detection of mobile malware using machine learning |
| topic | malware analysis and detection applied machine learning mobile security neural network ensemble classification botnet detection deep learning Android botnets convolutional neural networks dense neural networks recurrent neural networks long short-term memory gated recurrent unit CNN-LSTM CNN-GRU Android security malware detection code vulnerability machine learning malware static analysis dynamic analysis hybrid analysis security Monte-Carlo simulation reinforcement learning adversarial sample convolutional neural network Histogram of Oriented Gradients image processing android botnets digital forensic optimization multilayer perceptron salp swarm algorithm connection weights business email compromise (BEC) email phishing phishing detection machine learning (ML) systematic literature review steganography steganalysis polyglots neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | malware analysis and detection applied machine learning mobile security neural network ensemble classification botnet detection deep learning Android botnets convolutional neural networks dense neural networks recurrent neural networks long short-term memory gated recurrent unit CNN-LSTM CNN-GRU Android security malware detection code vulnerability machine learning malware static analysis dynamic analysis hybrid analysis security Monte-Carlo simulation reinforcement learning adversarial sample convolutional neural network Histogram of Oriented Gradients image processing android botnets digital forensic optimization multilayer perceptron salp swarm algorithm connection weights business email compromise (BEC) email phishing phishing detection machine learning (ML) systematic literature review steganography steganalysis polyglots neural networks n/a thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20230511_9783036571751_12 |