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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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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2023
Fag:
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
Online adgang:ONIX_20230511_9783036571751_12
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Summary: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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