High Accuracy Detection of Mobile Malware Using Machine Learning

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Information technology industries; adversarial sample; android botnets; Android botnets; Android security; applied machine learning; botnet detection; business email compromise (BEC); CNN-GRU; CNN-LSTM; code vulnerability; connection weights; convolutional neural network; convolutional neural networks; deep learning; dense neural networks; digital forensic; dynamic analysis; email phishing; ensemble classification; gated recurrent unit; Histogram of Oriented Gradients; hybrid analysis; image processing; long short-term memory; machine learning; machine learning (ML); malware; malware analysis and detection; malware detection; mobile security; Monte-Carlo simulation; multilayer perceptron; n/a; neural network; neural networks; optimization; phishing detection; polyglots; recurrent neural networks; reinforcement learning; salp swarm algorithm; security; static analysis; steganalysis; steganography; systematic literature review
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (226 p.)
• ISBN: 9783036571744; 9783036571751; books978-3-0365-7174-4
• Access: Open Access

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.