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Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks

https://doi.org/10.3390/math10040608
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-50127-7_11openalex-snapshot
Deep Learning for Classification of Malware System Call Sequences
inferredcites →https://doi.org/10.1007/978-3-319-56991-8_51openalex-snapshot
HADM: Hybrid Analysis for Detection of Malware
inferredcites →https://doi.org/10.1007/s11416-015-0261-zopenalex-snapshot
A comparison of static, dynamic, and hybrid analysis for malware detection
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.2021.102400openalex-snapshot
A novel framework for image-based malware detection with a deep neural network
inferredcites →https://doi.org/10.1016/j.future.2018.03.007openalex-snapshot
A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
inferredcites →https://doi.org/10.1016/j.future.2021.06.029openalex-snapshot
MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of Things
inferredcites →https://doi.org/10.1016/j.jisa.2021.102828openalex-snapshot
Malware classification and composition analysis: A survey of recent developments
inferredcites →https://doi.org/10.1016/j.jnca.2012.10.004openalex-snapshot
Classification of malware based on integrated static and dynamic features
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/compsac.2018.10315openalex-snapshot
Lightweight Classification of IoT Malware Based on Image Recognition
inferredcites →https://doi.org/10.1109/cspa.2018.8368693openalex-snapshot
One-dimensional convolutional neural networks for Android malware detection
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/icassp.2013.6638293openalex-snapshot
Large-scale malware classification using random projections and neural networks
inferredcites →https://doi.org/10.1109/icpads.2016.0155openalex-snapshot
IRMD: Malware Variant Detection Using Opcode Image Recognition
inferredcites →https://doi.org/10.1109/malware.2015.7413680openalex-snapshot
Deep neural network based malware detection using two dimensional binary program features
inferredcites →https://doi.org/10.1109/naecon.2017.8268747openalex-snapshot
Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset
inferredcites →https://doi.org/10.1109/ntms.2018.8328749openalex-snapshot
Malware Classification with Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/secpri.2001.924286openalex-snapshot
Data mining methods for detection of new malicious executables
inferredcites →https://doi.org/10.1109/spw.2019.00015openalex-snapshot
Exploring Adversarial Examples in Malware Detection
inferredcites →https://doi.org/10.1109/tii.2018.2822680openalex-snapshot
Detection of Malicious Code Variants Based on Deep Learning
inferredcites →https://doi.org/10.1145/1646396.1646421openalex-snapshot
Evaluation of GIST descriptors for web-scale image search
inferredcites →https://doi.org/10.1145/2016904.2016908openalex-snapshot
Malware images
inferredcites →https://doi.org/10.1145/2857705.2857713openalex-snapshot
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
inferredcites →https://doi.org/10.1145/3190645.3190692openalex-snapshot
Malware classification using deep learning methods
inferredcites →https://doi.org/10.1155/2018/7247095openalex-snapshot
Detecting Malware with an Ensemble Method Based on Deep Neural Network
inferredcites →https://doi.org/10.4018/978-1-5225-8241-0.ch011openalex-snapshot
Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security
inferredcites →https://doi.org/10.4236/jcc.2018.61016openalex-snapshot
Malware Images Classification Using Convolutional Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1605.07648openalex-snapshot
FractalNet: Ultra-Deep Neural Networks without Residuals
inferredcites →https://doi.org/10.48550/arxiv.1905.11946openalex-snapshot
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

What points at this work

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

inferred← citeshttps://doi.org/10.1002/spy2.444openalex-snapshot
IoT malware detection using static and dynamic analysis techniques: A systematic literature review
inferred← citeshttps://doi.org/10.1002/spy2.70000openalex-snapshot
Enhancing Malware Detection With Deep Learning Convolutional Neural Networks: Investigating the Impact of Image Size Variations
inferred← citeshttps://doi.org/10.1007/978-3-031-18409-3_3openalex-snapshot
Hand SOS Gesture Detection by Computer Vision
inferred← citeshttps://doi.org/10.1007/978-3-031-34969-0_6openalex-snapshot
Deep Learning for Windows Malware Analysis
inferred← citeshttps://doi.org/10.1007/978-3-031-78128-5_17openalex-snapshot
A Weighted Discrete Wavelet Transform-Based Capsule Network for Malware Classification
inferred← citeshttps://doi.org/10.1007/978-981-97-3741-3_36openalex-snapshot
Applying Convolutional Neural Networks (CNNs): A Machine Learning Method in Modern Sports
inferred← citeshttps://doi.org/10.1007/s10207-023-00712-zopenalex-snapshot
Malware classification approaches utilizing binary and text encoding of permissions
inferred← citeshttps://doi.org/10.1007/s10489-024-05707-4openalex-snapshot
Tri-channel visualised malicious code classification based on improved ResNet
inferred← citeshttps://doi.org/10.1007/s10489-024-05911-2openalex-snapshot
A malware visualization method based on transition probability matrix suitable for imbalanced family classification
inferred← citeshttps://doi.org/10.1007/s10586-024-04865-xopenalex-snapshot
FEdroid: a lightweight and interpretable machine learning-based android malware detection system
inferred← citeshttps://doi.org/10.1007/s10618-024-01078-zopenalex-snapshot
MIRACLE: Malware image recognition and classification by layered extraction
inferred← citeshttps://doi.org/10.1007/s11416-026-00668-3openalex-snapshot
Enhanced IoT malware detection via robust and scalable federated learning
inferred← citeshttps://doi.org/10.1007/s13202-024-01761-3openalex-snapshot
Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps
inferred← citeshttps://doi.org/10.1007/s42979-024-03123-6openalex-snapshot
Detecting Android Malware with Convolutional Neural Networks and Hilbert Space-Filling Curves
inferred← citeshttps://doi.org/10.1016/j.cherd.2024.04.033openalex-snapshot
Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure
inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112761openalex-snapshot
DEPN: Anti-obfuscation malware classification via dynamic manifold restoration
inferred← citeshttps://doi.org/10.1016/j.cose.2022.103084openalex-snapshot
MCTVD: A malware classification method based on three-channel visualization and deep learning
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103937openalex-snapshot
RETRACTED: TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107801openalex-snapshot
A novel machine learning approach for detecting first-time-appeared malware
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117957openalex-snapshot
Fusing feature engineering and deep learning: A case study for malware classification
inferred← citeshttps://doi.org/10.1016/j.ipm.2026.105155openalex-snapshot
A self-supervised approach to malware classification using hybrid VAE-Swin Transformer models
inferred← citeshttps://doi.org/10.1016/j.jisa.2022.103313openalex-snapshot
IMCLNet: A lightweight deep neural network for Image-based Malware Classification
inferred← citeshttps://doi.org/10.1016/j.measen.2022.100431openalex-snapshot
Attack prevention in IoT through hybrid optimization mechanism and deep learning framework
inferred← citeshttps://doi.org/10.1038/s41598-025-88130-0openalex-snapshot
Semantic lossless encoded image representation for malware classification
inferred← citeshttps://doi.org/10.1063/5.0248254openalex-snapshot
Role of artificial neural networks in fortifying cybersecurity: A review
inferred← citeshttps://doi.org/10.1109/access.2023.3343157openalex-snapshot
Bidirectional Current WP and CBAR Neural Network Model-Based Bearing Fault Diagnosis
inferred← citeshttps://doi.org/10.1109/access.2024.3403458openalex-snapshot
RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification
inferred← citeshttps://doi.org/10.1109/aiotcs58181.2022.00021openalex-snapshot
Applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects
inferred← citeshttps://doi.org/10.1109/iccc68994.2026.11511524openalex-snapshot
Identifying Malicious Software Using Neural Networks Based on Visual Representation and Static Analysis Techniques
inferred← citeshttps://doi.org/10.1109/icdcece60827.2024.10548601openalex-snapshot
Effective Machine Leaning Based Malware detection and Classification Using Improved Voting Method based Decision Tree
inferred← citeshttps://doi.org/10.1109/icitcom66635.2025.11265195openalex-snapshot
Robustness Evaluation of CNN-Based PE Malware Classifiers under Gradient-Based Adversarial Attacks
inferred← citeshttps://doi.org/10.1109/icspis57063.2022.10002693openalex-snapshot
Comparison of Feature Extraction and Classification Techniques of PE Malware
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On the Use of Deep Neural Networks for Security Vulnerabilities Detection in Smart Contracts
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Exploring A Novel Data Augmentation Strategy for Enhanced In-Vehicle Security Analysis
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Explainable AI-Driven Security Framework for Cyber-Physical Production Systems in Industry 4.0: Leveraging Immersive Embedded CIoT
inferred← citeshttps://doi.org/10.1109/tic68483.2026.11702958openalex-snapshot
Deep Learning Based Malware Image Classification Using Binary Image Representation for Secure Management Information Systems
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A Comparative Evaluation of Deep Learning Techniques for Smart Contract Vulnerability Classification
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An Efficient Convolutional Neural Network with Transfer Learning for Malware Classification
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Advanced Computational Modeling of CO2 Solubility in Ionic Liquids Using an XAI Methodology
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4745962/v1openalex-snapshot
FEdroid: Lightweight and Interpretable Detection of Android Malware Using Local Key Information and Feature Selection
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SFCWGAN-BiTCN with Sequential Features for Malware Detection
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A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
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Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
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AD-CapsFPN: An Asymmetric Dilated Convolutional Capsule Network with Feature Pyramid for Malware Classification
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MLP-Mixer-Autoencoder: A Lightweight Ensemble Architecture for Malware Classification
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A Blockchain and Federated Learning Framework for Image-Based IoT Malware Detection and Prevention
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Dynamic Extraction of Initial Behavior for Evasive Malware Detection
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One Filter Is All You Need: Understanding Pattern Recognition in 1D Convolutional Neural Networks
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