High Accuracy Detection of Mobile Malware Using Machine Learning

• Autres auteurs: Yerima, Suleiman (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Sujets: Information technology industries; 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
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

Résumé

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.

Description matérielle

1 electronic resource (226 p.)

ISBN

books978-3-0365-7174-4
9783036571751
9783036571744

Accès

Open Access