Research graph
References from A malware classification method based on directed API call relationships. Local targets link to admitted publications; unresolved targets remain external evidence.
A comprehensive survey on deep learning based malware detection techniques
10.1016/j.cosrev.2022.100529 · 2023 · External reference
Android malware detection methods based on convolutional neural network: a survey
10.1109/tetci.2023.3281833 · 2023 · External reference
10.12968/s1361-3723(22)70578-7
10.12968/s1361-3723(22)70578-7 · External reference
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A new deep boosted CNN and ensemble learning based IoT malware detection
10.1016/j.cose.2023.103385 · 2023 · External reference
An energy efficient local popularity based cooperative caching for mobile information centric networks
10.3390/su142013135 · 2022 · External reference
10.1038/s41598-022-18936-9
10.1038/s41598-022-18936-9 · External reference
A deep convolutional neural network stacked ensemble for malware threat classification in internet of things
10.1142/s0218126622503029 · 2022 · External reference
Android malware detection method based on CNN and DNN bybrid mechanism
10.1109/tii.2024.3363016 · 2024 · External reference
Development of a deep stacked ensemble with process based volatile memory forensics for platform independent malware detection and classification
10.1016/j.eswa.2023.119952 · 2023 · External reference
A comparison of static, dynamic, and hybrid analysis for malware detection
2017 · External reference
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FCSCNN
2022 · External reference
A novel deep framework for dynamic malware detection based on API sequence intrinsic features
10.1016/j.cose.2022.102686 · 2022 · External reference
API-MalDetect: automated malware detection framework for windows based on API calls and deep learning techniques
10.1016/j.jnca.2023.103704 · 2023 · External reference
CruParamer: learning on parameter-augmented API sequences for malware detection
10.1109/tifs.2022.3152360 · 2022 · External reference
DMalNet: dynamic malware analysis based on API feature engineering and graph learning
10.1016/j.cose.2022.102872 · 2022 · External reference
Malware detection using windows API sequence and machine learning
2012 · External reference
10.1109/ictai.2013.53
10.1109/ictai.2013.53 · External reference
A new malware detection system using machine learning techniques for API call sequences
10.1080/19361610.2018.1387734 · 2018 · External reference
Improved support vector machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification
10.1371/journal.pone.0295951 · 2024 · External reference
Malware API calls detection using hybrid logistic regression and RNN model
10.3390/app13095439 · 2023 · External reference
10.1109/itnec.2019.8728992
10.1109/itnec.2019.8728992 · External reference
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Intelligent mobile malware detection using permission requests and API calls
10.1016/j.future.2020.02.002 · 2020 · External reference
10.1109/itnec.2019.8728992
10.1109/itnec.2019.8728992 · External reference
Android malware detection using deep learning on API method sequences
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GDroid
2021 · External reference
Directed graph convolutional network
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Semi-supervised classification with graph convolutional networks
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Alibaba cloud malware detection based on behaviors
2018 · External reference
Data augmentation based malware detection using convolutional neural networks
10.7717/peerj-cs.346 · 2021 · External reference
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Android malware detection using complex-flows
10.1109/tmc.2018.2861405 · 2019 · External reference
Android malware detection based on system call sequences and LSTM
10.1007/s11042-017-5104-0 · 2019 · External reference
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Inductive representation learning on large graphs. Red Hook, NY
2017 · External reference
Graph attention networks
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Dynamic malware analysis based on API sequence semantic fusion
10.3390/app13116526 · 2023 · External reference
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Deep learning based Sequential model for malware analysis using Windows exe API Calls
10.7717/peerj-cs.285 · 2020 · External reference
