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Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features

https://doi.org/10.1109/access.2018.2870534

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-26190-4_28openalex-snapshot

Analyzing File-to-File Relation Network in Malware Detection

inferredcites →https://doi.org/10.1007/s00500-016-2283-yopenalex-snapshot

MOCDroid: multi-objective evolutionary classifier for Android malware detection

inferredcites →https://doi.org/10.1007/s10844-010-0148-xopenalex-snapshot

“Andromaly”: a behavioral malware detection framework for android devices

inferredcites →https://doi.org/10.1016/j.ins.2011.08.020openalex-snapshot

Opcode sequences as representation of executables for data-mining-based unknown malware detection

inferredcites →https://doi.org/10.1093/jigpal/jzx031openalex-snapshot

Using Dalvik opcodes for malware detection on android

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/cybersecpods.2016.7502343openalex-snapshot

N-opcode analysis for android malware classification and categorization

inferredcites →https://doi.org/10.1109/icccn.2016.7568542openalex-snapshot

Malware Variant Detection Using Opcode Image Recognition with Small Training Sets

inferredcites →https://doi.org/10.1109/iccs1.2017.8325997openalex-snapshot

Android malware detection a survey

inferredcites →https://doi.org/10.1109/icpads.2016.0155openalex-snapshot

IRMD: Malware Variant Detection Using Opcode Image Recognition

inferredcites →https://doi.org/10.1109/icse.2017.40openalex-snapshot

Adaptive Unpacking of Android Apps

inferredcites →https://doi.org/10.1109/malware.2017.8323954openalex-snapshot

Android malware family classification based on resource consumption over time

inferredcites →https://doi.org/10.1109/swat.1971.10openalex-snapshot

Depth-first search and linear graph algorithms

inferredcites →https://doi.org/10.1109/tbdata.2018.2790439openalex-snapshot

A Large-Scale Study of Android Malware Development Phenomenon on Public Malware Submission and Scanning Platform

inferredcites →https://doi.org/10.1109/tdsc.2013.40openalex-snapshot

Control Flow-Based Malware VariantDetection

inferredcites →https://doi.org/10.1109/tdsc.2017.2745575openalex-snapshot

Detection of Repackaged Android Malware with Code-Heterogeneity Features

inferredcites →https://doi.org/10.1109/tifs.2018.2806891openalex-snapshot

Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis

inferredcites →https://doi.org/10.1137/1.9781611972818.12openalex-snapshot

Polonium: Tera-Scale Graph Mining and Inference for Malware Detection

inferredcites →https://doi.org/10.1145/2046614.2046619openalex-snapshot

Crowdroid

inferredcites →https://doi.org/10.1145/2480362.2480701openalex-snapshot

Mobile-sandbox

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/10.1145/2623330.2623342openalex-snapshot

Guilt by association

inferredcites →https://doi.org/10.1145/2843859.2843860openalex-snapshot

OpSeq

inferredcites →https://doi.org/10.1145/3029806.3029823openalex-snapshot

Deep Android Malware Detection

inferredcites →https://doi.org/10.1145/3134600.3134604openalex-snapshot

Marmite

inferredcites →https://doi.org/10.1155/2018/7247095openalex-snapshot

Detecting Malware with an Ensemble Method Based on Deep Neural Network

inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot

Drebin: Effective and Explainable Detection of Android Malware in Your Pocket

inferredcites →https://doi.org/10.3115/v1/p14-1062openalex-snapshot

A Convolutional Neural Network for Modelling Sentences

What points at this work

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

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Privacy issues of android application permissions: A literature review

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Mutual Information and Feature Importance Gradient Boosting: Automatic byte n‐gram feature reranking for Android malware detection

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Transfer Learning for Internet of Things Malware Analysis

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Malware in the Connected World: Advanced Detection Techniques Using System Calls and Graph Analysis

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A Novel Approach for Analysing and Detection of Obfuscated Malware Payloads in Android Platform Using DexMonitor

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ELM-MVD: An Extreme Learning Machine Trained Model for Malware Variants Detection

inferred← citeshttps://doi.org/10.1007/978-981-16-9576-6_19openalex-snapshot

A Systematic Literature Review on the Mobile Malware Detection Methods

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A Review on Malware Variants Detection Techniques for Threat Intelligence in Resource Constrained Devices: Existing Approaches, Limitations and Future Direction

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On Static Android Malware Detection and Analysis: A Systematic Review

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HeuCrip: a malware detection approach for internet of battlefield things

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Android malware detection based on sensitive patterns

inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112058openalex-snapshot

Android malware detection through centrality analysis of applications network

inferred← citeshttps://doi.org/10.1016/j.cose.2019.04.005openalex-snapshot

A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding

inferred← citeshttps://doi.org/10.1016/j.cose.2024.103807openalex-snapshot

GSEDroid: GNN-based Android malware detection framework using lightweight semantic embedding

inferred← citeshttps://doi.org/10.1016/j.cosrev.2021.100373openalex-snapshot

Malicious application detection in android — A systematic literature review

inferred← citeshttps://doi.org/10.1049/sfw2.12083openalex-snapshot

Android malware detection via efficient application programming interface call sequences extraction and machine learning classifiers

inferred← citeshttps://doi.org/10.1051/matecconf/202030902002openalex-snapshot

Packed malware variants detection using deep belief networks

inferred← citeshttps://doi.org/10.1093/comjnl/bxac198openalex-snapshot

Android Malware Detection in Bytecode Level Using TF-IDF and XGBoost

inferred← citeshttps://doi.org/10.1109/access.2019.2919796openalex-snapshot

An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis

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A3CM: Automatic Capability Annotation for Android Malware

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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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FLSH: A Framework Leveraging Similarity Hashing for Android Malware and Variant Detection

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R+R: Revisiting Static Feature-Based Android Malware Detection Using Machine Learning

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Mitigating Malware Attacks using Machine Learning: A Review

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An Optimized Positive-Unlabeled Learning Method for Detecting a Large Scale of Malware Variants

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Power System Malware Detection Based on Deep Belief Network Classifier

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Combining Multimodal DNN and SigPid technique for detecting Malicious Android Apps

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Familial analysis of interface and program targeting noise contained malware using image processing

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BERTDeep-Ware: A Cross-architecture Malware Detection Solution for IoT Systems

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Opcode n-gram based Malware Classification in Android

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On Malware Detection in the Android Operating System

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On-Device Detection of Repackaged Android Malware via Traffic Clustering

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A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

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Software engineering techniques for statically analyzing mobile apps: research trends, characteristics, and potential for industrial adoption

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A Review and Case Study on Android Malware: Threat Model, Attacks, Techniques and Tools

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Ransomware Behavior Attack Construction via Graph Theory Approach

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An incremental malware detection model for meta-feature API and system call sequence

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An Android Malware Detection Approach Based on Summation of Multi-order Derivatives LSTM

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DCEL: Classifier Fusion Model for Android Malware Detection

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Android malware detection framework based on sensitive opcodes and deep reinforcement learning

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Byte-Level Function-Associated Method for Malware Detection

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GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking

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Is Malware Detection Needed for Android TV?

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Impact of Permission in Android on Data Security

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JDroid: Android malware detection using hybrid opcode feature vector

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