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Deep Learning for Android Malware Defenses: A Systematic Literature Review
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cpe.5308openalex-snapshot
Opcode sequence analysis of Android malware by a convolutional neural network
inferredcites →https://doi.org/10.1002/ett.3675openalex-snapshot
Android malware detection through generative adversarial networks
inferredcites →https://doi.org/10.1002/ett.3791openalex-snapshot
Detection of clone scammers in Android markets using IoT‐based edge computing
inferredcites →https://doi.org/10.1007/978-3-030-02450-5_11openalex-snapshot
CDGDroid: Android Malware Detection Based on Deep Learning Using CFG and DFG
inferredcites →https://doi.org/10.1007/978-3-030-13057-2_9openalex-snapshot
SeqDroid: Obfuscated Android Malware Detection Using Stacked Convolutional and Recurrent Neural Networks
inferredcites →https://doi.org/10.1007/978-3-030-37231-6_22openalex-snapshot
On Effectiveness of Adversarial Examples and Defenses for Malware Classification
inferredcites →https://doi.org/10.1007/978-3-030-80825-9_16openalex-snapshot
PetaDroid: Adaptive Android Malware Detection Using Deep Learning
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/10.1007/978-3-319-47121-1_5openalex-snapshot
DroidDelver: An Android Malware Detection System Using Deep Belief Network Based on API Call Blocks
inferredcites →https://doi.org/10.1007/978-3-319-53817-4_4openalex-snapshot
Word Embedding for Understanding Natural Language: A Survey
inferredcites →https://doi.org/10.1007/978-3-319-56991-8_51openalex-snapshot
HADM: Hybrid Analysis for Detection of Malware
inferredcites →https://doi.org/10.1007/978-3-319-60876-1_12openalex-snapshot
Deep Ground Truth Analysis of Current Android Malware
inferredcites →https://doi.org/10.1007/978-3-319-64701-2_14openalex-snapshot
DNA-Droid: A Real-Time Android Ransomware Detection Framework
inferredcites →https://doi.org/10.1007/978-3-319-66399-9_4openalex-snapshot
Adversarial Examples for Malware Detection
inferredcites →https://doi.org/10.1007/978-981-19-8991-9_29openalex-snapshot
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
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/s00500-019-04589-wopenalex-snapshot
DroidDeep: using Deep Belief Network to characterize and detect android malware
inferredcites →https://doi.org/10.1007/s00521-020-04831-9openalex-snapshot
On defending against label flipping attacks on malware detection systems
inferredcites →https://doi.org/10.1007/s00521-020-05195-wopenalex-snapshot
VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques
inferredcites →https://doi.org/10.1007/s10586-017-0944-yopenalex-snapshot
Deep neural architectures for large scale android malware analysis
inferredcites →https://doi.org/10.1007/s10586-019-03045-6openalex-snapshot
SysDroid: a dynamic ML-based android malware analyzer using system call traces
inferredcites →https://doi.org/10.1007/s10664-020-09843-6openalex-snapshot
Learning actionable analytics from multiple software projects
inferredcites →https://doi.org/10.1007/s10664-021-09955-7openalex-snapshot
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
inferredcites →https://doi.org/10.1007/s10796-020-10083-8openalex-snapshot
Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning
inferredcites →https://doi.org/10.1007/s11042-017-5104-0openalex-snapshot
Android malware detection based on system call sequences and LSTM
inferredcites →https://doi.org/10.1007/s11042-020-08804-xopenalex-snapshot
Adversarial android malware detection for mobile multimedia applications in IoT environments
inferredcites →https://doi.org/10.1007/s11416-019-00346-7openalex-snapshot
Deep learning for image-based mobile malware detection
inferredcites →https://doi.org/10.1007/s11431-020-1647-3openalex-snapshot
Pre-trained models for natural language processing: A survey
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/10.1007/s13042-020-01069-8openalex-snapshot
From static to dynamic word representations: a survey
inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-snapshot
Multilayer feedforward networks are universal approximators
inferredcites →https://doi.org/10.1016/j.adhoc.2020.102098openalex-snapshot
End-to-end malware detection for android IoT devices using deep learning
inferredcites →https://doi.org/10.1016/j.asoc.2020.106089openalex-snapshot
An adaptive framework against android privilege escalation threats using deep learning and semi-supervised approaches
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.comnet.2020.107639openalex-snapshot
Comparative analysis of feature representations and machine learning methods in Android family classification
inferredcites →https://doi.org/10.1016/j.cose.2018.11.001openalex-snapshot
Survey of machine learning techniques for malware analysis
inferredcites →https://doi.org/10.1016/j.cose.2019.101663openalex-snapshot
DL-Droid: Deep learning based android malware detection using real devices
inferredcites →https://doi.org/10.1016/j.cose.2020.101740openalex-snapshot
Byte-level malware classification based on markov images and deep learning
inferredcites →https://doi.org/10.1016/j.cose.2020.101792openalex-snapshot
AMalNet: A deep learning framework based on graph convolutional networks for malware detection
inferredcites →https://doi.org/10.1016/j.cose.2021.102198openalex-snapshot
Towards an interpretable deep learning model for mobile malware detection and family identification
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.cose.2021.102287openalex-snapshot
Challenges and pitfalls in malware research
inferredcites →https://doi.org/10.1016/j.diin.2018.01.007openalex-snapshot
MalDozer: Automatic framework for android malware detection using deep learning
inferredcites →https://doi.org/10.1016/j.future.2019.07.070openalex-snapshot
Static malware detection and attribution in android byte-code through an end-to-end deep system
inferredcites →https://doi.org/10.1016/j.infsof.2014.02.001openalex-snapshot
A systematic literature review of software requirements prioritization research
inferredcites →https://doi.org/10.1016/j.infsof.2017.04.001openalex-snapshot
Static analysis of android apps: A systematic literature review
inferredcites →https://doi.org/10.1016/j.jisa.2019.102423openalex-snapshot
Visualizing the outcome of dynamic analysis of Android malware with VizMal
inferredcites →https://doi.org/10.1016/j.jpdc.2019.11.001openalex-snapshot
Malware detection in mobile environments based on Autoencoders and API-images
inferredcites →https://doi.org/10.1016/j.jss.2019.04.065openalex-snapshot
Revisiting the impact of common libraries for android-related investigations
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.neucom.2018.09.102openalex-snapshot
Learning to detect Android malware via opcode sequences
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot
Deep learning in neural networks: An overview
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/10.1049/iet-ifs.2015.0211openalex-snapshot
Back‐propagation neural network on Markov chains from system call sequences: a new approach for detecting Android malware with system call sequences
inferredcites →https://doi.org/10.1049/iet-ifs.2019.0159openalex-snapshot
Parallel‐CNN network for malware detection
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/access.2019.2912210openalex-snapshot
AC-Net: Assessing the Consistency of Description and Permission in Android Apps
inferredcites →https://doi.org/10.1109/access.2019.2914311openalex-snapshot
Predicting the Impact of Android Malicious Samples via Machine Learning
inferredcites →https://doi.org/10.1109/access.2019.2946392openalex-snapshot
A3CM: Automatic Capability Annotation for Android Malware
inferredcites →https://doi.org/10.1109/access.2020.3006143openalex-snapshot
A Review of Android Malware Detection Approaches Based on Machine Learning
inferredcites →https://doi.org/10.1109/access.2020.3007571openalex-snapshot
An Android Malware Detection Approach Based on SIMGRU
inferredcites →https://doi.org/10.1109/access.2020.3028370openalex-snapshot
Review of Android Malware Detection Based on Deep Learning
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/ase51524.2021.9678580openalex-snapshot
Explainable AI for Software Engineering
inferredcites →https://doi.org/10.1109/asiajcis.2012.18openalex-snapshot
DroidMat: Android Malware Detection through Manifest and API Calls Tracing
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/bigdata47090.2019.9006100openalex-snapshot
Are We Really Protected? An Investigation into the Play Protect Service
inferredcites →https://doi.org/10.1109/candar.2019.00019openalex-snapshot
Function Identification in Android Binaries with Deep Learning
inferredcites →https://doi.org/10.1109/cec.2017.7969501openalex-snapshot
Evolving Deep Neural Networks architectures for Android malware classification
inferredcites →https://doi.org/10.1109/cns48642.2020.9162341openalex-snapshot
Hybrid Analysis of Android Apps for Security Vetting using Deep Learning
inferredcites →https://doi.org/10.1109/comst.2014.2386139openalex-snapshot
Android Security: A Survey of Issues, Malware Penetration, and Defenses
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-snapshot
Aggregated Residual Transformations for Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00572openalex-snapshot
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
inferredcites →https://doi.org/10.1109/eisic.2012.34openalex-snapshot
A Machine Learning Approach to Android Malware Detection
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/eurosp.2018.00040openalex-snapshot
DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks
inferredcites →https://doi.org/10.1109/eurosp48549.2020.00018openalex-snapshot
Evaluating Explanation Methods for Deep Learning in Security
inferredcites →https://doi.org/10.1109/glocom.2017.8254503openalex-snapshot
Reinforcement Learning Based Mobile Offloading for Cloud-Based Malware Detection
inferredcites →https://doi.org/10.1109/hicss.2012.114openalex-snapshot
Android: Static Analysis Using Similarity Distance
inferredcites →https://doi.org/10.1109/icacci.2017.8126084openalex-snapshot
Deep android malware detection and classification
inferredcites →https://doi.org/10.1109/icc42927.2021.9500425openalex-snapshot
Robustness of Image-based Android Malware Detection Under Adversarial Attacks
inferredcites →https://doi.org/10.1109/icce.2018.8326293openalex-snapshot
DDefender: Android application threat detection using static and dynamic analysis
inferredcites →https://doi.org/10.1109/iceccs.2019.00014openalex-snapshot
MobiDroid: A Performance-Sensitive Malware Detection System on Mobile Platform
inferredcites →https://doi.org/10.1109/icit.2019.8755048openalex-snapshot
Mobile Malware Detection: An Analysis of Deep Learning Model
inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-snapshot
BranchyNet: Fast inference via early exiting from deep neural networks
inferredcites →https://doi.org/10.1109/icse.2015.48openalex-snapshot
IccTA: Detecting Inter-Component Privacy Leaks in Android Apps
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966078openalex-snapshot
Classification of Android apps and malware using deep neural networks
inferredcites →https://doi.org/10.1109/iscc.2017.8024568openalex-snapshot
DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data
inferredcites →https://doi.org/10.1109/iscc43571.2018openalex-snapshot
2018 IEEE Symposium on Computers and Communications (ISCC)
inferredcites →https://doi.org/10.1109/iscc47284.2019.8969711openalex-snapshot
FSNet: Android Malware Detection with Only One Feature
inferredcites →https://doi.org/10.1109/iwqos.2018.8624143openalex-snapshot
Deep and Broad Learning Based Detection of Android Malware via Network Traffic
inferredcites →https://doi.org/10.1109/iwqos39950.2017openalex-snapshot
2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)
inferredcites →https://doi.org/10.1109/jiot.2019.2909745openalex-snapshot
EveDroid: Event-Aware Android Malware Detection Against Model Degrading for IoT Devices
inferredcites →https://doi.org/10.1109/jsyst.2019.2906120openalex-snapshot
Adversarial-Example Attacks Toward Android Malware Detection System
inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
inferredcites →https://doi.org/10.1109/msp.2017.2743240openalex-snapshot
Deep Reinforcement Learning: A Brief Survey
inferredcites →https://doi.org/10.1109/msr52588.2021.00049openalex-snapshot
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
inferredcites →https://doi.org/10.1109/ntms.2016.7792435openalex-snapshot
ANASTASIA: ANdroid mAlware detection using STatic analySIs of Applications
inferredcites →https://doi.org/10.1109/sarnof.2016.7846747openalex-snapshot
DroidDeepLearner: Identifying Android malware using deep learning
inferredcites →https://doi.org/10.1109/sds.2019.8768729openalex-snapshot
A Survey on Android Malware Detection Techniques Using Machine Learning Algorithms
inferredcites →https://doi.org/10.1109/snpd.2018.8441128openalex-snapshot
Tuning Deep Learning Performance for Android Malware Detection
inferredcites →https://doi.org/10.1109/sp.2012.16openalex-snapshot
Dissecting Android Malware: Characterization and Evolution
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/spw.2019.00018openalex-snapshot
MaxNet: Neural Network Architecture for Continuous Detection of Malicious Activity
inferredcites →https://doi.org/10.1109/tase.2019.00-20openalex-snapshot
Android Malware Family Classification and Characterization Using CFG and DFG
inferredcites →https://doi.org/10.1109/tase43516.2018openalex-snapshot
2018 International Symposium on Theoretical Aspects of Software Engineering (TASE)
inferredcites →https://doi.org/10.1109/tc.2021.3082002openalex-snapshot
Malware Analysis By Combining Multiple Detectors and Observation Windows
inferredcites →https://doi.org/10.1109/tdsc.2020.3005088openalex-snapshot
SDAC: A Slow-Aging Solution for Android Malware Detection Using Semantic Distance Based API Clustering
inferredcites →https://doi.org/10.1109/tdsc.2021.3094824openalex-snapshot
Backdoor Attack on Machine Learning Based Android Malware Detectors
inferredcites →https://doi.org/10.1109/tetci.2019.2910243openalex-snapshot
An Enhanced Stacked LSTM Method With No Random Initialization for Malware Threat Hunting in Safety and Time-Critical Systems
inferredcites →https://doi.org/10.1109/tifs.2017.2656460openalex-snapshot
Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting
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.2019.2932228openalex-snapshot
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
inferredcites →https://doi.org/10.1109/tifs.2019.2947861openalex-snapshot
Familial Clustering for Weakly-Labeled Android Malware Using Hybrid Representation Learning
inferredcites →https://doi.org/10.1109/tifs.2020.3003571openalex-snapshot
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
inferredcites →https://doi.org/10.1109/tifs.2020.3021924openalex-snapshot
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
inferredcites →https://doi.org/10.1109/tifs.2020.3025436openalex-snapshot
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices
inferredcites →https://doi.org/10.1109/tii.2020.3038745openalex-snapshot
Deep-Learning–Based App Sensitive Behavior Surveillance for Android Powered Cyber–Physical Systems
inferredcites →https://doi.org/10.1109/tmc.2020.3007260openalex-snapshot
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store
inferredcites →https://doi.org/10.1109/tnnls.2018.2886017openalex-snapshot
Adversarial Examples: Attacks and Defenses for Deep Learning
inferredcites →https://doi.org/10.1109/tnse.2021.3051354openalex-snapshot
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1109/trustcom.2016.0070openalex-snapshot
A Deep Learning Approach to Android Malware Feature Learning and Detection
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00022openalex-snapshot
Malware Detection with Malware Images using Deep Learning Techniques
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00044openalex-snapshot
Selective Adversarial Learning for Mobile Malware
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00047openalex-snapshot
Deep Android Malware Classification with API-Based Feature Graph
inferredcites →https://doi.org/10.1109/tse.2019.2901679openalex-snapshot
Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark
inferredcites →https://doi.org/10.1109/tse.2020.2982385openalex-snapshot
An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
inferredcites →https://doi.org/10.1109/tse.2020.3023177openalex-snapshot
Predicting Defective Lines Using a Model-Agnostic Technique
inferredcites →https://doi.org/10.1109/tse.2021.3062968openalex-snapshot
Defect Reduction Planning (Using TimeLIME)
inferredcites →https://doi.org/10.1109/tse.2021.3070559openalex-snapshot
SQAPlanner: Generating Data-Informed Software Quality Improvement Plans
inferredcites →https://doi.org/10.1109/tsmc.2019.2958382openalex-snapshot
A Lightweight On-Device Detection Method for Android Malware
inferredcites →https://doi.org/10.1109/tst.2016.7399288openalex-snapshot
Droiddetector: android malware characterization and detection using deep learning
inferredcites →https://doi.org/10.1109/wcnc45663.2020.9120537openalex-snapshot
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning
inferredcites →https://doi.org/10.1109/wcnc45663.2020.9120748openalex-snapshot
Sadroid: A Deep Classification Model For Android Malware Detection Based On Semantic Analysis
inferredcites →https://doi.org/10.1109/wcnc45663.2020.9120765openalex-snapshot
End-Edge Coordinated Inference for Real-Time BYOD Malware Detection using Deep Learning
inferredcites →https://doi.org/10.1109/wcncw.2019.8902627openalex-snapshot
Android Malware Detection Based on System Calls Analysis and CNN Classification
inferredcites →https://doi.org/10.1109/wiw.2016.040openalex-snapshot
Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs
inferredcites →https://doi.org/10.1145/2046614.2046618openalex-snapshot
A survey of mobile malware in the wild
inferredcites →https://doi.org/10.1145/2939672.2939751openalex-snapshot
Asymmetric Transitivity Preserving Graph Embedding
inferredcites →https://doi.org/10.1145/3017427openalex-snapshot
The Evolution of Android Malware and Android Analysis Techniques
inferredcites →https://doi.org/10.1145/3029806.3029823openalex-snapshot
Deep Android Malware Detection
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3073559openalex-snapshot
A Survey on Malware Detection Using Data Mining Techniques
inferredcites →https://doi.org/10.1145/3110025.3116211openalex-snapshot
Deep Neural Networks for Automatic Android Malware Detection
inferredcites →https://doi.org/10.1145/3230833.3232823openalex-snapshot
Detection of Obfuscation Techniques in Android Applications
inferredcites →https://doi.org/10.1145/3236009openalex-snapshot
A Survey of Methods for Explaining Black Box Models
inferredcites →https://doi.org/10.1145/3236024.3236045openalex-snapshot
FraudDroid: automated ad fraud detection for Android apps
inferredcites →https://doi.org/10.1145/3264437.3264476openalex-snapshot
Applying deep learning techniques for Android malware detection
inferredcites →https://doi.org/10.1145/3297280.3297396openalex-snapshot
A scalable and accurate feature representation method for identifying malicious mobile applications
inferredcites →https://doi.org/10.1145/3324884.3416582openalex-snapshot
Automated third-party library detection for Android applications
inferredcites →https://doi.org/10.1145/3329786openalex-snapshot
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey
inferredcites →https://doi.org/10.1145/3338501.3357374openalex-snapshot
Malware Detection on Highly Imbalanced Data through Sequence Modeling
inferredcites →https://doi.org/10.1145/3342195.3387530openalex-snapshot
Experiences of landing machine learning onto market-scale mobile malware detection
inferredcites →https://doi.org/10.1145/3345768.3355915openalex-snapshot
A Transparent and Multimodal Malware Detection Method for Android Apps
inferredcites →https://doi.org/10.1145/3359786openalex-snapshot
Techniques for interpretable machine learning
inferredcites →https://doi.org/10.1145/3366423.3380242openalex-snapshot
MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps
inferredcites →https://doi.org/10.1145/3368089.3409733openalex-snapshot
DENAS: automated rule generation by knowledge extraction from neural networks
inferredcites →https://doi.org/10.1145/3372297.3417291openalex-snapshot
Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware
inferredcites →https://doi.org/10.1145/3374664.3375730openalex-snapshot
Understanding Privacy Awareness in Android App Descriptions Using Deep Learning
inferredcites →https://doi.org/10.1145/3377811.3380354openalex-snapshot
Unsuccessful story about few shot malware family classification and siamese network to the rescue
inferredcites →https://doi.org/10.1145/3382158openalex-snapshot
A Data-driven Characterization of Modern Android Spyware
inferredcites →https://doi.org/10.1145/3384544.3384546openalex-snapshot
A Systematical Study for Deep Learning Based Android Malware Detection
inferredcites →https://doi.org/10.1145/3387905.3388596openalex-snapshot
Experimental comparison of features and classifiers for Android malware detection
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inferredcites →https://doi.org/10.1155/2018/5249190openalex-snapshot
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