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...

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Bibliographic Details
Other Authors: Yerima, Suleiman (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
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Online Access:DOAB: download the publication
DOAB: description of the publication
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245 1 0 |a High Accuracy Detection of Mobile Malware Using Machine Learning 
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520 |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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546 |a English 
650 7 |a Information technology industries  |2 bicssc 
653 |a malware analysis and detection 
653 |a applied machine learning 
653 |a mobile security 
653 |a neural network 
653 |a ensemble classification 
653 |a botnet detection 
653 |a deep learning 
653 |a Android botnets 
653 |a convolutional neural networks 
653 |a dense neural networks 
653 |a recurrent neural networks 
653 |a long short-term memory 
653 |a gated recurrent unit 
653 |a CNN-LSTM 
653 |a CNN-GRU 
653 |a Android security 
653 |a malware detection 
653 |a code vulnerability 
653 |a machine learning 
653 |a malware 
653 |a static analysis 
653 |a dynamic analysis 
653 |a hybrid analysis 
653 |a security 
653 |a Monte-Carlo simulation 
653 |a reinforcement learning 
653 |a adversarial sample 
653 |a convolutional neural network 
653 |a Histogram of Oriented Gradients 
653 |a image processing 
653 |a android botnets 
653 |a digital forensic 
653 |a optimization 
653 |a multilayer perceptron 
653 |a salp swarm algorithm 
653 |a connection weights 
653 |a business email compromise (BEC) 
653 |a email phishing 
653 |a phishing detection 
653 |a machine learning (ML) 
653 |a systematic literature review 
653 |a steganography 
653 |a steganalysis 
653 |a polyglots 
653 |a neural networks 
653 |a n/a 
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