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A novel framework for image-based malware detection with a deep neural network

https://doi.org/10.1016/j.cose.2021.102400

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-40667-1_20openalex-snapshot

MtNet: A Multi-Task Neural Network for Dynamic Malware Classification

inferredcites →https://doi.org/10.1007/978-3-642-15512-3_17openalex-snapshot

Hybrid Analysis and Control of Malware

inferredcites →https://doi.org/10.1007/s10207-014-0242-0openalex-snapshot

Malware analysis using visualized images and entropy graphs

inferredcites →https://doi.org/10.1007/s11265-020-01588-1openalex-snapshot

A Method for Windows Malware Detection Based on Deep Learning

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/10.1007/s11416-018-0324-zopenalex-snapshot

Analysis of ResNet and GoogleNet models for malware detection

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.asoc.2016.11.046openalex-snapshot

Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization

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.compeleceng.2019.03.015openalex-snapshot

Identification of malicious code variants based on image visualization

inferredcites →https://doi.org/10.1016/j.cose.2017.10.007openalex-snapshot

A malware detection method based on family behavior graph

inferredcites →https://doi.org/10.1016/j.cose.2017.11.010openalex-snapshot

Efficient dynamic malware analysis using virtual time control mechanics

inferredcites →https://doi.org/10.1016/j.cose.2018.02.006openalex-snapshot

Auto-detection of sophisticated malware using lazy-binding control flow graph and deep learning

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.cose.2019.101592openalex-snapshot

Neural malware analysis with attention mechanism

inferredcites →https://doi.org/10.1016/j.cose.2019.101682openalex-snapshot

A novel method for malware detection on ML-based visualization technique

inferredcites →https://doi.org/10.1016/j.cose.2020.101748openalex-snapshot

Image-Based malware classification using ensemble of CNN architectures (IMCEC)

inferredcites →https://doi.org/10.1016/j.cose.2020.101895openalex-snapshot

Multiclass malware classification via first- and second-order texture statistics

inferredcites →https://doi.org/10.1016/j.cose.2021.102247openalex-snapshot

Malware detection employed by visualization and deep neural network

inferredcites →https://doi.org/10.1016/j.diin.2018.04.024openalex-snapshot

Deep learning at the shallow end: Malware classification for non-domain experts

inferredcites →https://doi.org/10.1016/j.ins.2018.04.092openalex-snapshot

Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders

inferredcites →https://doi.org/10.1016/j.jisa.2019.06.006openalex-snapshot

A hybrid deep learning image-based analysis for effective malware detection

inferredcites →https://doi.org/10.1016/j.knosys.2016.09.004openalex-snapshot

FindMal: A file-to-file social network based malware detection framework

inferredcites →https://doi.org/10.1016/j.neucom.2019.11.051openalex-snapshot

Adversarial attacks on Faster R-CNN object detector

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

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/icassp.2017.7952603openalex-snapshot

Malware classification with LSTM and GRU language models and a character-level CNN

inferredcites →https://doi.org/10.1109/icawst.2019.8923568openalex-snapshot

Applying Convolutional Neural Network for Malware Detection

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/ictai.2013.53openalex-snapshot

Machine Learning for Android Malware Detection Using Permission and API Calls

inferredcites →https://doi.org/10.1109/kse.2018.8573374openalex-snapshot

Comparison of Three Deep Learning-based Approaches for IoT Malware Detection

inferredcites →https://doi.org/10.1109/msp.2007.45openalex-snapshot

Toward Automated Dynamic Malware Analysis Using CWSandbox

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/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/naecon46414.2019.9058025openalex-snapshot

Convolutional Neural Networks as Classification Tools and Feature Extractors for Distinguishing Malware Programs

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/tdsc.2018.2884928openalex-snapshot

Deep Learning and Visualization for Identifying Malware Families

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/tpami.2019.2913372openalex-snapshot

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00109openalex-snapshot

A Multi-channel Visualization Method for Malware Classification Based on Deep Learning

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

Malware images

inferredcites →https://doi.org/10.1145/3351881openalex-snapshot

A Multilabel Fuzzy Relevance Clustering System for Malware Attack Attribution in the Edge Layer of Cyber-Physical Networks

inferredcites →https://doi.org/10.1155/2021/6658842openalex-snapshot

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

inferredcites →https://doi.org/10.1609/aaai.v32i1.11409openalex-snapshot

Classification of Malware by Using Structural Entropy on Convolutional Neural Networks

inferredcites →https://doi.org/10.1609/aaai.v35i9.16947openalex-snapshot

Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in the COVID-19 Era and Beyond

inferredcites →https://doi.org/10.18653/v1/p16-2034openalex-snapshot

Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

inferredcites →https://doi.org/10.24963/ijcai.2019/522openalex-snapshot

Heterogeneous Graph Matching Networks for Unknown Malware Detection

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Malware Detection in Images Using CNN

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Comparative Analysis of DL and Traditional ML Methods for Malware Image Classification

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A Malicious Code Family Classification Method Based on RGB Images and Lightweight Model

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AI-powered malware intelligence: a review of machine and deep learning paradigms in detection and classification

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Tri-channel visualised malicious code classification based on improved ResNet

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Performance evaluation of deep neural network on malware detection: visual feature approach

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Deep learning vs. adversarial noise: a battle in malware image analysis

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Peer Review #1 of "Artificial intelligence-driven malware detection framework for internet of things environment (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1366v0.1/reviews/2openalex-snapshot

Peer Review #2 of "Artificial intelligence-driven malware detection framework for internet of things environment (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1366v0.1/reviews/3openalex-snapshot

Peer Review #3 of "Artificial intelligence-driven malware detection framework for internet of things environment (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1366v0.2/reviews/2openalex-snapshot

Peer Review #2 of "Artificial intelligence-driven malware detection framework for internet of things environment (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.1366v0.2/reviews/3openalex-snapshot

Peer Review #3 of "Artificial intelligence-driven malware detection framework for internet of things environment (v0.2)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.1366openalex-snapshot

Artificial intelligence-driven malware detection framework for internet of things environment

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.