On the public citation graph

Multimodal Deep Learning for Android Malware Classification

0 signed · 83 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_1openalex-snapshot

CBAM: Convolutional Block Attention Module

inferredcites →https://doi.org/10.1007/978-3-030-87839-9_4openalex-snapshot

DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode

inferredcites →https://doi.org/10.1007/s00500-019-03940-5openalex-snapshot

Deep learning for effective Android malware detection using API call graph embeddings

inferredcites →https://doi.org/10.1007/s11416-011-0151-yopenalex-snapshot

Malware classification based on call graph clustering

inferredcites →https://doi.org/10.1007/s11416-018-0323-0openalex-snapshot

Using convolutional neural networks for classification of malware represented as images

inferredcites →https://doi.org/10.1016/j.compeleceng.2021.107143openalex-snapshot

A comprehensive survey of tools and techniques mitigating computer and mobile malware attacks

inferredcites →https://doi.org/10.1016/j.cose.2020.101873openalex-snapshot

HYDRA: A multimodal deep learning framework for malware classification

inferredcites →https://doi.org/10.1016/j.cose.2021.102264openalex-snapshot

GDroid: Android malware detection and classification with graph convolutional network

inferredcites →https://doi.org/10.1016/j.inffus.2004.04.004openalex-snapshot

Diversity creation methods: a survey and categorisation

inferredcites →https://doi.org/10.1016/j.inffus.2024.102662openalex-snapshot

Detecting Android malware: A multimodal fusion method with fine-grained feature

inferredcites →https://doi.org/10.1016/j.patcog.2017.10.013openalex-snapshot

Recent advances in convolutional neural networks

inferredcites →https://doi.org/10.1023/a:1022859003006openalex-snapshot

Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

inferredcites →https://doi.org/10.1109/access.2021.3049819openalex-snapshot

Detection, Traceability, and Propagation of Mobile Malware Threats

inferredcites →https://doi.org/10.1109/bwcca.2010.85openalex-snapshot

Malware Obfuscation Techniques: A Brief Survey

inferredcites →https://doi.org/10.1109/cicybs.2013.6597204openalex-snapshot

Image visualization based malware detection

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot

Xception: Deep Learning with Depthwise Separable Convolutions

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot

MobileNetV2: Inverted Residuals and Linear Bottlenecks

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01271openalex-snapshot

What Makes Training Multi-Modal Classification Networks Hard?

inferredcites →https://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00094openalex-snapshot

Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning

inferredcites →https://doi.org/10.1109/dsc54232.2022.9888878openalex-snapshot

Graph Neural Network-based Android Malware Classification with Jumping Knowledge

inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

inferredcites →https://doi.org/10.1109/iccwamtip53232.2021.9674165openalex-snapshot

Ensemble Method For Net Traffic Classification Based On Deep Learning

inferredcites →https://doi.org/10.1109/icmla.2017.00-19openalex-snapshot

Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network

inferredcites →https://doi.org/10.1109/icnn.1996.548872openalex-snapshot

Generalization error of ensemble estimators

inferredcites →https://doi.org/10.1109/infoct.2019.8711179openalex-snapshot

TFDroid: Android Malware Detection by Topics and Sensitive Data Flows Using Machine Learning Techniques

inferredcites →https://doi.org/10.1109/msp.2017.2693418openalex-snapshot

Geometric Deep Learning: Going beyond Euclidean data

inferredcites →https://doi.org/10.1109/msp.2017.2738401openalex-snapshot

Deep Multimodal Learning: A Survey on Recent Advances and Trends

inferredcites →https://doi.org/10.1109/sds57534.2023.00012openalex-snapshot

A Graph-Representation-Learning Framework for Supporting Android Malware Identification and Polymorphic Evolution

inferredcites →https://doi.org/10.1109/tifs.2018.2866319openalex-snapshot

A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

inferredcites →https://doi.org/10.1109/tifs.2018.2879302openalex-snapshot

DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling

inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-snapshot

A Comprehensive Survey on Graph Neural Networks

inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot

Representation Learning: A Review and New Perspectives

inferredcites →https://doi.org/10.1109/tpami.2018.2798607openalex-snapshot

Multimodal Machine Learning: A Survey and Taxonomy

inferredcites →https://doi.org/10.1109/tpds.2020.3046092openalex-snapshot

Systematically Landing Machine Learning onto Market-Scale Mobile Malware Detection

inferredcites →https://doi.org/10.1111/coin.12532openalex-snapshot

A two‐stage deep learning framework for image‐based android malware detection and variant classification

inferredcites →https://doi.org/10.1145/2016904.2016908openalex-snapshot

Malware images

inferredcites →https://doi.org/10.1145/2046684.2046689openalex-snapshot

A comparative assessment of malware classification using binary texture analysis and dynamic analysis

inferredcites →https://doi.org/10.1145/2517312.2517315openalex-snapshot

Structural detection of android malware using embedded call graphs

inferredcites →https://doi.org/10.1145/2592791.2592796openalex-snapshot

Rage against the virtual machine

inferredcites →https://doi.org/10.1145/2857705.2857713openalex-snapshot

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

inferredcites →https://doi.org/10.1145/2901739.2903508openalex-snapshot

AndroZoo

inferredcites →https://doi.org/10.1145/3017427openalex-snapshot

The Evolution of Android Malware and Android Analysis Techniques

inferredcites →https://doi.org/10.1145/3029806.3029824openalex-snapshot

Scalable Function Call Graph-based Malware Classification

inferredcites →https://doi.org/10.1145/3292500.3330701openalex-snapshot

Optuna

inferredcites →https://doi.org/10.1145/3412841.3442080openalex-snapshot

Detecting and categorizing Android malware with graph neural networks

inferredcites →https://doi.org/10.1145/3479393openalex-snapshot

Ransomware Mitigation in the Modern Era: A Comprehensive Review, Research Challenges, and Future Directions

inferredcites →https://doi.org/10.1145/3511808.3557533openalex-snapshot

MalNet: A Large-Scale Image Database of Malicious Software

inferredcites →https://doi.org/10.1145/3597503.3623320openalex-snapshot

A Comprehensive Study of Learning-based Android Malware Detectors under Challenging Environments

inferredcites →https://doi.org/10.1145/3603273.3635055openalex-snapshot

A Multi-modality Feature Fusion Method for Android Malware Detection

inferredcites →https://doi.org/10.1145/3691340openalex-snapshot

Ransomware Reloaded: Re-examining Its Trend, Research and Mitigation in the Era of Data Exfiltration

inferredcites →https://doi.org/10.1155/2013/704504openalex-snapshot

A Review of Data Fusion Techniques

inferredcites →https://doi.org/10.18653/v1/2022.acl-long.499openalex-snapshot

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

inferredcites →https://doi.org/10.21105/joss.00861openalex-snapshot

UMAP: Uniform Manifold Approximation and Projection

inferredcites →https://doi.org/10.21528/cbic2021-32openalex-snapshot

Hunting Android Malware Using Multimodal Deep Learning and Hybrid Analysis Data

inferredcites →https://doi.org/10.3390/app132312629openalex-snapshot

Android Malware Detection Based on Hypergraph Neural Networks

inferredcites →https://doi.org/10.3390/info12050185openalex-snapshot

A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

inferredcites →https://doi.org/10.3390/sym12071128openalex-snapshot

Two Anatomists Are Better than One—Dual-Level Android Malware Detection

inferredcites →https://doi.org/10.4230/lipics.sea.2024.20openalex-snapshot

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot

Semi-Supervised Classification with Graph Convolutional Networks

inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot

A Unified Approach to Interpreting Model Predictions

inferredcites →https://doi.org/10.48550/arxiv.1709.05281openalex-snapshot

AndroZoo++: Collecting Millions of Android Apps and Their Metadata for the Research Community

inferredcites →https://doi.org/10.48550/arxiv.1806.03536openalex-snapshot

Representation Learning on Graphs with Jumping Knowledge Networks

inferredcites →https://doi.org/10.48550/arxiv.1806.08804openalex-snapshot

Hierarchical Graph Representation Learning with Differentiable Pooling

inferredcites →https://doi.org/10.48550/arxiv.2010.13902openalex-snapshot

Graph Contrastive Learning with Augmentations

inferredcites →https://doi.org/10.48550/arxiv.2011.07682openalex-snapshot

A Large-Scale Database for Graph Representation Learning

inferredcites →https://doi.org/10.48550/arxiv.2102.05152openalex-snapshot

On Explainability of Graph Neural Networks via Subgraph Explorations

inferredcites →https://doi.org/10.5220/0006642503790385openalex-snapshot

Impact of Code Obfuscation on Android Malware Detection based on Static and Dynamic Analysis

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-95-6419-4_6openalex-snapshot

MEMC: A Lightweight Image-Based Malware Classifier Based on Improved Shuffle and Coordination Attention

inferred← citeshttps://doi.org/10.1007/s10586-026-06667-9openalex-snapshot

Multimodal vision transformer framework for android malware detection via 2D grayscale and QR code ımage representations, feature fusion, and RFE-optimized majority voting

inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104388openalex-snapshot

A multimodal fusion method for android malware detection based on modal independence preservation

inferred← citeshttps://doi.org/10.1016/j.procs.2025.09.043openalex-snapshot

AndroMesh: Android Malware Detection Using Graph Neural Networks with Function-Aware Local-Global Topology

inferred← citeshttps://doi.org/10.1038/s41598-026-40655-8openalex-snapshot

Hierarchical malware detection, family identification, and variant attribution using CNN-based hybrid models on grayscale executable images

inferred← citeshttps://doi.org/10.1109/acit69831.2026.11700033openalex-snapshot

Multimodal Method for Detecting Hidden Threats in Multimedia Objects Based on Cross-Modal Feature Alignment

inferred← citeshttps://doi.org/10.1109/cars67163.2025.11337448openalex-snapshot

Attention-Enabled Fusion Based Multi Class Malware Family Classification Using Large Scale BCCC-Mal-NetMem-2025 Dataset

inferred← citeshttps://doi.org/10.1109/icaaic64647.2025.11331245openalex-snapshot

Attention-Guided Ensemble Meta-CNN for Real-Time Android Malware Detection from APK Byte-Images

inferred← citeshttps://doi.org/10.1109/qpain69676.2026.11546632openalex-snapshot

Deep Ensemble Learning for Malware Classification Using CNN-Based Stacking and Blending

inferred← citeshttps://doi.org/10.1186/s42400-025-00481-3openalex-snapshot

VIMAR: vision-language informed malware analysis and reasoning model

inferred← citeshttps://doi.org/10.21203/rs.3.rs-9576397/v1openalex-snapshot

XAI-1DSig: Explainable and Robust 1D Signal-Based IoT Malware Classification

inferred← citeshttps://doi.org/10.3390/electronics15102167openalex-snapshot

AI-Driven Rule-Based Feature Scoring for Explainable and Adversarially Robust Android Malware Detection

inferred← citeshttps://doi.org/10.4018/979-8-2600-1970-2.ch002openalex-snapshot

Explainability in Malware and Ransomware Detection

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.