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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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Bibliographic Details
Main Author: Yerima, Suleiman
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Machine Learning
Android Botnets
Botnet Detection
Cnn-gru
Code Vulnerability
Image Processing
N/a
Malware Analysis And Detection
Deep Learning
Polyglots
Gated Recurrent Unit
Neural Networks
Email Phishing
Systematic Literature Review
Phishing Detection
Steganalysis
Salp Swarm Algorithm
Machine Learning (ml)
Mobile Security
Ensemble Classification
Cnn-lstm
Reinforcement Learning
Information Technology Industries / Bicssc
Static Analysis
Connection Weights
Adversarial Sample
Recurrent Neural Networks
Dense Neural Networks
Security
Convolutional Neural Networks
Monte-carlo Simulation
Histogram Of Oriented Gradients
Applied Machine Learning
Malware Detection
Android Security
Long Short-term Memory
Hybrid Analysis
Multilayer Perceptron
Digital Forensic
Dynamic Analysis
Convolutional Neural Network
Malware
Optimization
Business Email Compromise (bec)
Steganography
Neural Network
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (226 p.)
ISBN:9783036571744
9783036571751
books978-3-0365-7174-4

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