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Classification and Analysis of Android Malware Images Using Feature Fusion Technique

https://doi.org/10.1109/access.2021.3090998
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mp.12453openalex-snapshot
A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets
inferredcites →https://doi.org/10.1007/978-3-030-70866-5_18openalex-snapshot
A Semi-automated Approach for Identification of Trends in Android Ransomware Literature
inferredcites →https://doi.org/10.1007/978-981-10-8968-8_27openalex-snapshot
Android Anti-malware Techniques and Its Vulnerabilities: A Survey
inferredcites →https://doi.org/10.1007/s11042-020-09637-4openalex-snapshot
Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer
inferredcites →https://doi.org/10.1007/s11416-018-0324-zopenalex-snapshot
Analysis of ResNet and GoogleNet models for malware detection
inferredcites →https://doi.org/10.1007/s11704-017-6493-yopenalex-snapshot
Fingerprinting Android malware families
inferredcites →https://doi.org/10.1007/s12652-018-0803-6openalex-snapshot
Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
inferredcites →https://doi.org/10.1007/s12652-020-02196-4openalex-snapshot
Android malware detection method based on bytecode image
inferredcites →https://doi.org/10.1016/j.adhoc.2020.102154openalex-snapshot
Malware detection in industrial internet of things based on hybrid image visualization and deep learning model
inferredcites →https://doi.org/10.1016/j.comnet.2020.107138openalex-snapshot
IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture
inferredcites →https://doi.org/10.1016/j.cose.2018.04.005openalex-snapshot
Malware identification using visualization images and deep learning
inferredcites →https://doi.org/10.1016/j.iot.2019.100153openalex-snapshot
Analysis of internet of things malware using image texture features and machine learning techniques
inferredcites →https://doi.org/10.1016/j.jnca.2016.08.022openalex-snapshot
The rise of “malware”: Bibliometric analysis of malware study
inferredcites →https://doi.org/10.1016/j.microrel.2019.01.007openalex-snapshot
An Android mutation malware detection based on deep learning using visualization of importance from codes
inferredcites →https://doi.org/10.1016/j.patcog.2013.01.003openalex-snapshot
Image region description using orthogonal combination of local binary patterns enhanced with color information
inferredcites →https://doi.org/10.1016/j.procs.2017.08.216openalex-snapshot
Evaluating Convolutional Neural Network for Effective Mobile Malware Detection
inferredcites →https://doi.org/10.1016/s0079-6123(06)55002-2openalex-snapshot
Chapter 2 Building the gist of a scene: the role of global image features in recognition
inferredcites →https://doi.org/10.1023/a:1011139631724openalex-snapshot
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
inferredcites →https://doi.org/10.1049/iet-ipr.2018.5054openalex-snapshot
Late fusion of deep learning and handcrafted visual features for biomedical image modality classification
inferredcites →https://doi.org/10.1109/access.2021.3061592openalex-snapshot
Wireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model
inferredcites →https://doi.org/10.1109/ase.2019.00155openalex-snapshot
An Image-Inspired and CNN-Based Android Malware Detection Approach
inferredcites →https://doi.org/10.1109/bigdata.2017.8258512openalex-snapshot
Binary malware image classification using machine learning with local binary pattern
inferredcites →https://doi.org/10.1109/bigdata.2018.8622324openalex-snapshot
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
inferredcites →https://doi.org/10.1109/cicybs.2013.6597204openalex-snapshot
Image visualization based malware detection
inferredcites →https://doi.org/10.1109/icasid.2017.8285743openalex-snapshot
Image classification for malware detection using extremely randomized trees
inferredcites →https://doi.org/10.1109/icdmai.2017.8073489openalex-snapshot
Malware class recognition using image processing techniques
inferredcites →https://doi.org/10.1109/icmew46912.2020.9106021openalex-snapshot
Multi-CNN Feature Fusion for Efficient EEG Classification
inferredcites →https://doi.org/10.1109/icpr.2010.751openalex-snapshot
Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition
inferredcites →https://doi.org/10.1109/icssa.2017.18openalex-snapshot
CNN-Based Android Malware Detection
inferredcites →https://doi.org/10.1109/ictc.2017.8190895openalex-snapshot
Malware detection using malware image and deep learning
inferredcites →https://doi.org/10.1109/itact.2015.7492653openalex-snapshot
Malware analysis and classification using Artificial Neural Network
inferredcites →https://doi.org/10.1109/naecon.2016.7856826openalex-snapshot
Performance analysis of machine learning and pattern recognition algorithms for Malware classification
inferredcites →https://doi.org/10.1109/ntms.2018.8328749openalex-snapshot
Malware Classification with Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/spw.2016.25openalex-snapshot
DroidScribe: Classifying Android Malware Based on Runtime Behavior
inferredcites →https://doi.org/10.1109/tii.2018.2822680openalex-snapshot
Detection of Malicious Code Variants Based on Deep Learning
inferredcites →https://doi.org/10.1109/tse.2016.2615307openalex-snapshot
A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Software
inferredcites →https://doi.org/10.1145/2016904.2016908openalex-snapshot
Malware images
inferredcites →https://doi.org/10.1145/2307636.2307663openalex-snapshot
RiskRanker
inferredcites →https://doi.org/10.1145/3264437.3264476openalex-snapshot
Applying deep learning techniques for Android malware detection
inferredcites →https://doi.org/10.13005/ojcst/10.02.20openalex-snapshot
Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier
inferredcites →https://doi.org/10.13089/jkiisc.2016.26.1.197openalex-snapshot
Visualized Malware Classification Based-on Convolutional Neural Network
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
inferredcites →https://doi.org/10.30534/ijatcse/2020/288942020openalex-snapshot
Classification of Android Malware using its Image Sections
inferredcites →https://doi.org/10.32604/cmc.2021.014504openalex-snapshot
Understanding Research Trends in Android Malware Research Using Information Modelling Techniques
inferredcites →https://doi.org/10.3390/app10093166openalex-snapshot
Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition
inferredcites →https://doi.org/10.3390/app9204209openalex-snapshot
Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
inferredcites →https://doi.org/10.3390/e22040484openalex-snapshot
Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures
inferredcites →https://doi.org/10.3390/s20247013openalex-snapshot
Deep Feature Extraction and Classification of Android Malware Images
inferredcites →https://doi.org/10.36227/techrxiv.13359767.v1openalex-snapshot
Chimera: An Android Malware Detection Method Based on Multimodal Deep Learning and Hybrid Analysis
inferredcites →https://doi.org/10.3837/tiis.2018.01.022openalex-snapshot
Android malicious code Classification using Deep Belief Network
inferredcites →https://doi.org/10.48550/arxiv.1801.00318openalex-snapshot
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Malware Detection in Android Mobile Devices by Applying Swarm Intelligence Optimization and Machine Learning for API Calls
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TL‐GNN : Android Malware Detection Using Transfer Learning
inferred← citeshttps://doi.org/10.1002/cpe.7180openalex-snapshot
AMD‐CNN: Android malware detection via feature graph and convolutional neural networks
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Explainable machine learning in cybersecurity: A survey
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Relevance of Imaged-Based Representation for Android Malware Detection
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Android Malware Detection Using Smali Codes
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Android Malware Detection Method Based on Multi-feature Extraction and SPP-Net
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A Novel Federated Learning Approach to Memory-Based Malware Detection Model Using RMS Propagation and Sequential Classifier
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Behavioral Computing for Human Factor Security and Safety in Cyberfinance
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Static Adversarial Method
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A Systematic Review on Skin Cancer Classification and Novel Approaches in Deep Learning
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Spatial-Based Big Data and Large-Scale Network Management
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malC: A Novel Deep Learning Architecture for Malware Classification
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EfficientNet deep learning meta-classifier approach for image-based android malware detection
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Blockchain DrivenAccess control architecture for the internet of things
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ACMFNN: A Novel Design of an Augmented Convolutional Model for Intelligent Cross-Domain Malware Localization via Forensic Neural Networks
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Android Ransomware Analysis Using Convolutional Neural Network and Fuzzy Hashing Features
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Implementation of a Hybrid Ensemble Approach for Image-Based Malware Detection
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Multi-feature image analysis for Android malware classification using convolutional neural networks
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Towards a Reliable Hierarchical Android Malware Detection Through Image-based CNN
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