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MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-04283-1_6openalex-snapshot
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
inferredcites →https://doi.org/10.1007/978-3-540-72559-6_6openalex-snapshot
Acceptable System Response Times for TV and DVR
inferredcites →https://doi.org/10.1007/978-3-642-21599-5_7openalex-snapshot
Taming Information-Stealing Smartphone Applications (on Android)
inferredcites →https://doi.org/10.1007/978-3-642-33704-8_21openalex-snapshot
MADAM: A Multi-level Anomaly Detector for Android Malware
inferredcites →https://doi.org/10.1007/978-3-642-54568-9_14openalex-snapshot
AppGuard – Fine-Grained Policy Enforcement for Untrusted Android Applications
inferredcites →https://doi.org/10.1016/j.jnca.2013.05.008openalex-snapshot
Detecting mobile malware threats to homeland security through static analysis
inferredcites →https://doi.org/10.1109/iaw.2005.1495942openalex-snapshot
Learning classifiers for misuse and anomaly detection using a bag of system calls representation
inferredcites →https://doi.org/10.1109/icmla.2011.55openalex-snapshot
Dimensionality Reduction by Unsupervised K-Nearest Neighbor Regression
inferredcites →https://doi.org/10.1109/mc.2014.169openalex-snapshot
Thwarting Obfuscated Malware via Differential Fault Analysis
inferredcites →https://doi.org/10.1109/pst.2008.25openalex-snapshot
Mimicry Attacks Demystified: What Can Attackers Do to Evade Detection?
inferredcites →https://doi.org/10.1109/sp.2012.16openalex-snapshot
Dissecting Android Malware: Characterization and Evolution
inferredcites →https://doi.org/10.1109/tdsc.2013.58openalex-snapshot
Effective Risk Communication for Android Apps
inferredcites →https://doi.org/10.1109/tdsc.2014.2300482openalex-snapshot
MOSES: Supporting and Enforcing Security Profiles on Smartphones
inferredcites →https://doi.org/10.1109/tit.1967.1053964openalex-snapshot
Nearest neighbor pattern classification
inferredcites →https://doi.org/10.1145/1030083.1030088openalex-snapshot
Testing network-based intrusion detection signatures using mutant exploits
inferredcites →https://doi.org/10.1145/1127345.1127348openalex-snapshot
Anomalous system call detection
inferredcites →https://doi.org/10.1145/2046614.2046624openalex-snapshot
Practical and lightweight domain isolation on Android
inferredcites →https://doi.org/10.1145/2335356.2335360openalex-snapshot
Android permissions
inferredcites →https://doi.org/10.1145/2494522openalex-snapshot
TaintDroid
inferredcites →https://doi.org/10.1145/2517312.2517315openalex-snapshot
Structural detection of android malware using embedded call graphs
inferredcites →https://doi.org/10.1145/2619091openalex-snapshot
TaintDroid
inferredcites →https://doi.org/10.1145/2660267.2660359openalex-snapshot
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs
inferredcites →https://doi.org/10.1145/2664243.2664245openalex-snapshot
Design and implementation of an Android host-based intrusion prevention system
inferredcites →https://doi.org/10.1145/2664243.2664265openalex-snapshot
Android security framework
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
inferredcites →https://doi.org/10.5555/1763017.1763025openalex-snapshot
Acceptable system response times for TV and DVR
inferredcites →https://doi.org/10.5555/1924943.1924971openalex-snapshot
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.3247628openalex-extracted
Behavior-Based Security for Mobile Devices Using Machine Learning Techniques
inferred← citeshttps://doi.org/10.2139/ssrn.3328497openalex-extracted
Malware Detection Using Machine Learning Algorithms and Reverse Engineering of Android Java Code
inferred← citeshttps://doi.org/10.2139/ssrn.4656881openalex-extracted
inferred← citeshttps://doi.org/10.1002/9781394196470.ch6openalex-snapshot
Malware Analysis Techniques in Android‐Based Smartphone Applications
inferred← citeshttps://doi.org/10.1002/cpe.5308openalex-snapshot
Opcode sequence analysis of Android malware by a convolutional neural network
inferred← citeshttps://doi.org/10.1002/cpe.6272openalex-snapshot
A survey on analysis and detection of Android ransomware
inferred← citeshttps://doi.org/10.1002/cpe.6571openalex-snapshot
Security‐centric ranking algorithm and two privacy scores to mitigate intrusive apps
inferred← citeshttps://doi.org/10.1002/ett.3935openalex-snapshot
A survey of security and privacy issues in the Internet of Things from the layered context
inferred← citeshttps://doi.org/10.1002/int.22529openalex-snapshot
Hybrid sequence‐based Android malware detection using natural language processing
inferred← citeshttps://doi.org/10.1002/int.23094openalex-snapshot
CNN‐ and GAN‐based classification of malicious code families: A code visualization approach
inferred← citeshttps://doi.org/10.1002/widm.70119openalex-snapshot
Systematic Analysis of Machine Learning, Deep Learning, and Hybrid Models for Android and IoT Ecosystems: A Review
inferred← citeshttps://doi.org/10.1007/978-3-030-00012-7_10openalex-snapshot
Mass Discovery of Android Malware Behavioral Characteristics for Detection Consideration
inferred← citeshttps://doi.org/10.1007/978-3-030-01535-0_19openalex-snapshot
Android Malware Detection: A Survey
inferred← citeshttps://doi.org/10.1007/978-3-030-05057-3_32openalex-snapshot
SoProtector: Securing Native C/C++ Libraries for Mobile Applications
inferred← citeshttps://doi.org/10.1007/978-3-030-05063-4_14openalex-snapshot
MulAV: Multilevel and Explainable Detection of Android Malware with Data Fusion
inferred← citeshttps://doi.org/10.1007/978-3-030-30619-9_6openalex-snapshot
CatraDroid: A Call Trace Driven Detection of Malicious Behaiviors in Android Applications
inferred← citeshttps://doi.org/10.1007/978-3-030-33596-0_21openalex-snapshot
Security Vulnerabilities and Issues of Traditional Wireless Sensors Networks in IoT
inferred← citeshttps://doi.org/10.1007/978-3-030-36056-6_11openalex-snapshot
Android Botnet Detection by Classification Techniques
inferred← citeshttps://doi.org/10.1007/978-3-030-50323-9_10openalex-snapshot
DroidAutoML: A Microservice Architecture to Automate the Evaluation of Android Machine Learning Detection Systems
inferred← citeshttps://doi.org/10.1007/978-3-030-52856-0_7openalex-snapshot
A Large-Scale Investigation to Identify the Pattern of Permissions in Obfuscated Android Malwares
inferred← citeshttps://doi.org/10.1007/978-3-030-62582-5_5openalex-snapshot
A Survey of Intelligent Techniques for Android Malware Detection
inferred← citeshttps://doi.org/10.1007/978-3-030-63092-8_54openalex-snapshot
Classification Method for Malware Detection on Android Devices
inferred← citeshttps://doi.org/10.1007/978-3-030-74575-2_7openalex-snapshot
Revisiting the Approaches, Datasets and Evaluation Parameters to Detect Android Malware: A Comparative Study from State-of-Art
inferred← citeshttps://doi.org/10.1007/978-3-030-74664-3_2openalex-snapshot
Background and Related Work
inferred← citeshttps://doi.org/10.1007/978-3-030-87049-2_25openalex-snapshot
Malware Forensics: Legacy Solutions, Recent Advances, and Future Challenges
inferred← citeshttps://doi.org/10.1007/978-3-030-92635-9_22openalex-snapshot
MFF-AMD: Multivariate Feature Fusion for Android Malware Detection
inferred← citeshttps://doi.org/10.1007/978-3-030-95918-0_1openalex-snapshot
Android Malware Detection Using API Calls: A Comparison of Feature Selection and Machine Learning Models
inferred← citeshttps://doi.org/10.1007/978-3-030-96737-6_11openalex-snapshot
Creation of a Dataset Modeling the Behavior of Malware Affecting the Confidentiality of Data Managed by IoT Devices
inferred← citeshttps://doi.org/10.1007/978-3-031-15030-2_10openalex-snapshot
Android Malware Detection Using Deep Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-23095-0_7openalex-snapshot
DNNdroid: Android Malware Detection Framework Based on Federated Learning and Edge Computing
inferred← citeshttps://doi.org/10.1007/978-3-031-80408-3_17openalex-snapshot
Adversarial Malware Detection
inferred← citeshttps://doi.org/10.1007/978-3-032-16808-5_27openalex-snapshot
KG-AndroidDefender: Knowledge Graph Embedding for Explainable Malware Family Classification
inferred← citeshttps://doi.org/10.1007/978-3-032-17834-3_10openalex-snapshot
PermDroid: A Privacy-Preserving Framework for Android Malware Detection Using Federated Learning
inferred← citeshttps://doi.org/10.1007/978-3-032-22114-8_38openalex-snapshot
A Hybrid Machine Learning Approach for Detection of Malicious Android Apps
inferred← citeshttps://doi.org/10.1007/978-3-032-35579-9_28openalex-snapshot
Tapis: Android Tracing Tool for Framework-Wide Scalable Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-51966-1_1openalex-snapshot
MalProfiler: Automatic and Effective Classification of Android Malicious Apps in Behavioral Classes
inferred← citeshttps://doi.org/10.1007/978-3-319-72598-7_12openalex-snapshot
Malicious Application Detection on Android Smartphones with Enhanced Static-Dynamic Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-93563-8_14openalex-snapshot
Design and Implementation of Android Container Monitoring Server and Agent
inferred← citeshttps://doi.org/10.1007/978-981-10-5421-1_15openalex-snapshot
Low-Complexity Signature-Based Malware Detection for IoT Devices
inferred← citeshttps://doi.org/10.1007/978-981-10-5508-9_20openalex-snapshot
An Investigation of the Classifiers to Detect Android Malicious Apps
inferred← citeshttps://doi.org/10.1007/978-981-13-2826-8_28openalex-snapshot
DroidGene: Detecting Android Malware Using Its Malicious Gene
inferred← citeshttps://doi.org/10.1007/978-981-13-6861-5_38openalex-snapshot
A Survey on the Detection of Android Malicious Apps
inferred← citeshttps://doi.org/10.1007/978-981-13-7166-0_14openalex-snapshot
Evolution of Malware and Its Detection Techniques
inferred← citeshttps://doi.org/10.1007/978-981-13-9181-1_53openalex-snapshot
StackDroid: Evaluation of a Multi-level Approach for Detecting the Malware on Android Using Stacked Generalization
inferred← citeshttps://doi.org/10.1007/978-981-15-1275-9_17openalex-snapshot
Host-Server-Based Malware Detection System for Android Platforms Using Machine Learning
inferred← citeshttps://doi.org/10.1007/978-981-15-5232-8_56openalex-snapshot
An Improved Ensemble Based Machine Learning Technique for Efficient Malware Classification
inferred← citeshttps://doi.org/10.1007/978-981-15-6318-8_42openalex-snapshot
A Large-Scale Investigation to Identify the Pattern of App Component in Obfuscated Android Malwares
inferred← citeshttps://doi.org/10.1007/978-981-15-6648-6_20openalex-snapshot
Signature Based Malicious Behavior Detection in Android
inferred← citeshttps://doi.org/10.1007/978-981-15-7804-5_26openalex-snapshot
Machine Learning-Based Lightweight Android Malware Detection System with Static Features
inferred← citeshttps://doi.org/10.1007/978-981-16-1866-6_10openalex-snapshot
MDTA: A New Approach of Supervised Machine Learning for Android Malware Detection and Threat Attribution Using Behavioral Reports
inferred← citeshttps://doi.org/10.1007/978-981-16-3097-2_24openalex-snapshot
How Efficient Is Blockchain While Dealing with Android Malware? A Review Paper
inferred← citeshttps://doi.org/10.1007/978-981-16-3961-6_28openalex-snapshot
Survey for Detection and Analysis of Android Malware(s) Through Artificial Intelligence Techniques
inferred← citeshttps://doi.org/10.1007/978-981-16-5529-6_71openalex-snapshot
Detection of Attacks Using Multilayer Perceptron Algorithm
inferred← citeshttps://doi.org/10.1007/978-981-16-7985-8_102openalex-snapshot
A Critical Survey on Machine Learning Paradigms to Forecast Software Defects by Using Testing Parameters
inferred← citeshttps://doi.org/10.1007/978-981-19-2600-6_6openalex-snapshot
Review on Android Malware Detection System
inferred← citeshttps://doi.org/10.1007/978-981-19-4522-9_10openalex-snapshot
A Critical Survey on Machine Learning Paradigms to Forecast Software Defects by Using Testing Parameters
inferred← citeshttps://doi.org/10.1007/978-981-19-5443-6_58openalex-snapshot
An Effectual Analytics and Approach for Avoidance of Malware in Android Using Deep Neural Networks
inferred← citeshttps://doi.org/10.1007/978-981-95-6209-1_23openalex-snapshot
Dual Modal Featuring Scheme for Learning Based Android Malware Prevention
inferred← citeshttps://doi.org/10.1007/978-981-96-3284-8_8openalex-snapshot
Malware Detection Application for Intrusion Detection and Prevention
inferred← citeshttps://doi.org/10.1007/978-981-96-5194-8_5openalex-snapshot
Behavioral Structure Computing
inferred← citeshttps://doi.org/10.1007/978-981-96-8350-5_56openalex-snapshot
Advanced Android Malware Detection Via Image Analysis and Hybrid Transfer-Ensemble Learning
inferred← citeshttps://doi.org/10.1007/978-981-97-4928-7_4openalex-snapshot
Android Malware Detection Using Machine Learning
inferred← citeshttps://doi.org/10.1007/978-981-97-8031-0_60openalex-snapshot
Malware Detection Using Machine Learning Algorithms in Android
inferred← citeshttps://doi.org/10.1007/978-981-97-8666-4_37openalex-snapshot
An Assessment of the Recognition and Identification of Malware for Android Employing Optimized Choice of Features Based on Genetic Algorithms
inferred← citeshttps://doi.org/10.1007/978-981-99-3656-4_31openalex-snapshot
Three-Layered Hybrid Analysis Technique for Android Malware Detection
inferred← citeshttps://doi.org/10.1007/978-981-99-5257-1_2openalex-snapshot
Vertical Association Modeling: Latent Interaction Modeling
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Horizontal Association Modeling: Deep Relation Modeling
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DeMAndApp: Detecting Malicious Android App
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Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning
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DroidDeep: using Deep Belief Network to characterize and detect android malware
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Ask a(n)droid to tell you the odds: probabilistic security-by-contract for mobile devices
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AdvAttack: Adversarial attack against machine learning-based Android malware detection models
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SOMDROID: android malware detection by artificial neural network trained using unsupervised learning
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TriDroid: a triage and classification framework for fast detection of mobile threats in android markets
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ASSCA: API sequence and statistics features combined architecture for malware detection
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Regression coefficients as triad scale for malware detection
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Leveraging ontologies and machine-learning techniques for malware analysis into Android permissions ecosystems
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JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters
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S3Feature: A static sensitive subgraph-based feature for android malware detection
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An in-depth review of machine learning based Android malware detection
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A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learning
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GSEDroid: GNN-based Android malware detection framework using lightweight semantic embedding
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FAMCF: A few-shot Android malware family classification framework
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MCGDroid: An android malware classification method based on multi-feature class-call graph characterization
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ActDroid: An active learning framework for android malware detection
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A survey of malware detection in Android apps: Recommendations and perspectives for future research
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Behaviour analysis of inter-app communication using a lightweight monitoring app for malware detection
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Breaking the structure of MaMaDroid
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Detection approaches for android malware: Taxonomy and review analysis
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RansomDroid: Forensic analysis and detection of Android Ransomware using unsupervised machine learning technique
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Risk analysis of Android applications: A user-centric solution
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Characterizing Android apps’ behavior for effective detection of malapps at large scale
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A hybrid-multi filter-wrapper framework to identify run-time behaviour for fast malware detection
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Similarity-based Android malware detection using Hamming distance of static binary features
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A study of the relationship of malware detection mechanisms using Artificial Intelligence
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A study of run-time behavioral evolution of benign versus malicious apps in android
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A Systematic Overview of Android Malware Detection
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Ensemble Framework Combining Family Information for Android Malware Detection
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HealthDroid: malware detection in healthcare Android application using BERT
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SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System
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Malware Threats and Detection for Industrial Mobile-IoT Networks
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A Novel Dynamic Android Malware Detection System With Ensemble Learning
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Malytics: A Malware Detection Scheme
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Deep Learning Approach for Intelligent Intrusion Detection System
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A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features
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Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions
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An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis
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Machine Learning Based File Entropy Analysis for Ransomware Detection in Backup Systems
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SMASH: A Malware Detection Method Based on Multi-Feature Ensemble Learning
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Identifying Malicious Software Using Deep Residual Long-Short Term Memory
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A Comprehensive Review on Malware Detection Approaches
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A Systematic Literature Review of Android Malware Detection Using Static Analysis
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A Review of Android Malware Detection Approaches Based on Machine Learning
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FAMD: A Fast Multifeature Android Malware Detection Framework, Design, and Implementation
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AdMat: A CNN-on-Matrix Approach to Android Malware Detection and Classification
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Android Malware Detection Based on Composition Ratio of Permission Pairs
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Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review
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Crystal Ball: From Innovative Attacks to Attack Effectiveness Classifier
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Performance Evaluation of Open-Source Endpoint Detection and Response Combining Google Rapid Response and Osquery for Threat Detection
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A Formal Method for Description and Decision of Android Apps Behavior Based on Process Algebra
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A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features
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Evolving Malware and DDoS Attacks: Decadal Longitudinal Study
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Hybrid Android Malware Detection: A Review of Heuristic-Based Approach
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Maloid-DS: Labeled Dataset for Android Malware Forensics
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Device Risk Analysis Protocol for SMS-Based OTP Authentication
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Android Ransomware Detection using Machine Learning Techniques: A Comparative Analysis on GPU and CPU
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Behavior-Based Malware Detection System Approach For Mobile Security Using Machine Learning
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Mitigating Malware Attacks using Machine Learning: A Review
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MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis
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Enhanced Android Malware Detection: An SVM-Based Machine Learning Approach
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Droid-NNet: Deep Learning Neural Network for Android Malware Detection
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Are We Really Protected? An Investigation into the Play Protect Service
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Heterogeneous Graph Matching Networks: Application to Unknown Malware Detection
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Android-based Cryptocurrency Wallets: Attacks and Countermeasures
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