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EC2: Ensemble Clustering and Classification for Predicting Android Malware Families

https://doi.org/10.1109/tdsc.2017.2739145

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What this work says

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

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DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android

inferredcites →https://doi.org/10.1007/978-3-319-08509-8_4openalex-snapshot

AndRadar: Fast Discovery of Android Applications in Alternative Markets

inferredcites →https://doi.org/10.1007/978-3-319-11203-9_10openalex-snapshot

DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications

inferredcites →https://doi.org/10.1007/978-3-319-26362-5_18openalex-snapshot

HelDroid: Dissecting and Detecting Mobile Ransomware

inferredcites →https://doi.org/10.1007/978-3-540-70542-0_6openalex-snapshot

Learning and Classification of Malware Behavior

inferredcites →https://doi.org/10.1007/bf01908075openalex-snapshot

Comparing partitions

inferredcites →https://doi.org/10.1007/s11416-008-0084-2openalex-snapshot

Code obfuscation techniques for metamorphic viruses

inferredcites →https://doi.org/10.1016/j.cose.2015.02.007openalex-snapshot

Stealth attacks: An extended insight into the obfuscation effects on Android malware

inferredcites →https://doi.org/10.1016/j.eswa.2013.07.106openalex-snapshot

Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families

inferredcites →https://doi.org/10.1017/cbo9780511809071openalex-snapshot

Introduction to Information Retrieval

inferredcites →https://doi.org/10.1088/1742-5468/2005/09/p09008openalex-snapshot

Comparing community structure identification

inferredcites →https://doi.org/10.1109/compsac.2015.103openalex-snapshot

MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis

inferredcites →https://doi.org/10.1109/malware.2015.7413693openalex-snapshot

Clustering android malware families by http traffic

inferredcites →https://doi.org/10.1109/milcom.2016.7795300openalex-snapshot

Crafting adversarial input sequences for recurrent neural networks

inferredcites →https://doi.org/10.1109/secpri.1996.502675openalex-snapshot

A sense of self for Unix processes

inferredcites →https://doi.org/10.1109/sp.2012.16openalex-snapshot

Dissecting Android Malware: Characterization and Evolution

inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot

Towards Evaluating the Robustness of Neural Networks

inferredcites →https://doi.org/10.1109/spw.2016.25openalex-snapshot

DroidScribe: Classifying Android Malware Based on Runtime Behavior

inferredcites →https://doi.org/10.1109/tkde.2014.2320725openalex-snapshot

Malware Propagation in Large-Scale Networks

inferredcites →https://doi.org/10.1109/tkde.2016.2554119openalex-snapshot

GenPerm: A Unified Method for Detecting Non-Overlapping and Overlapping Communities

inferredcites →https://doi.org/10.1109/tse.2016.2615307openalex-snapshot

A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Software

inferredcites →https://doi.org/10.1145/1143844.1143865openalex-snapshot

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inferredcites →https://doi.org/10.1145/1277741.1277918openalex-snapshot

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inferredcites →https://doi.org/10.1145/1835804.1835820openalex-snapshot

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inferredcites →https://doi.org/10.1145/2020408.2020448openalex-snapshot

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

Crowdroid

inferredcites →https://doi.org/10.1145/2393596.2393627openalex-snapshot

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inferredcites →https://doi.org/10.1145/2480362.2480706openalex-snapshot

Slicing droids

inferredcites →https://doi.org/10.1145/2484313.2484355openalex-snapshot

DroidChameleon

inferredcites →https://doi.org/10.1145/2556464.2556467openalex-snapshot

DroidLegacy

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/10.1145/2666652.2666666openalex-snapshot

Poisoning behavioral malware clustering

inferredcites →https://doi.org/10.1145/2835776.2835834openalex-snapshot

Ensemble Models for Data-driven Prediction of Malware Infections

inferredcites →https://doi.org/10.1145/2857705.2857719openalex-snapshot

Inferring the Detection Logic and Evaluating the Effectiveness of Android Anti-Virus Apps

inferredcites →https://doi.org/10.1145/2857705.2857737openalex-snapshot

Evaluating Analysis Tools for Android Apps

inferredcites →https://doi.org/10.1145/2875475.2875487openalex-snapshot

Evaluation of Android Malware Detection Based on System Calls

inferredcites →https://doi.org/10.1145/2996758.2996769openalex-snapshot

Prescience

inferredcites →https://doi.org/10.1145/3029806.3029825openalex-snapshot

DroidSieve

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/10.1504/ijsnm.2015.069776openalex-snapshot

Generalised measures for the evaluation of community detection methods

inferredcites →https://doi.org/10.48550/arxiv.1312.3245openalex-snapshot

The Company You Keep: Mobile Malware Infection Rates and Inexpensive Risk Indicators

inferredcites →https://doi.org/10.48550/arxiv.1606.04435openalex-snapshot

Adversarial Perturbations Against Deep Neural Networks for Malware Classification

inferredcites →https://doi.org/10.5121/ijaia.2013.4411openalex-snapshot

Static Malware Detection System Using Data Mining Methods

inferredcites →https://doi.org/10.5220/0005809205420547openalex-snapshot

Identification of Android Malware Families with Model Checking

inferredcites →https://doi.org/10.5555/1248547.1248646openalex-snapshot

Learning to Detect and Classify Malicious Executables in the Wild

inferredcites →https://doi.org/10.5860/choice.46-2715openalex-snapshot

Introduction to information retrieval

What points at this work

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

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Machine intelligence approaches for preventing adversarial malware attacks in intrusion detection systems: A systematic review

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metaNet: Interpretable unknown mobile malware identification with a novel meta-features mining algorithm

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Scalable and robust unsupervised Android malware fingerprinting using community-based network partitioning

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Explainability in AI-based behavioral malware detection systems

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RansomDroid: Forensic analysis and detection of Android Ransomware using unsupervised machine learning technique

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DL-AMDet: Deep learning-based malware detector for android

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Multifamily Classification of Android Malware With a Fuzzy Strategy to Resist Polymorphic Familial Variants

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Prototype Cross Platform Oriented on Cybersecurity, Virtual Connectivity, Big Data and Artificial Intelligence Control

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Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

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Efficient Deep Learning Network With Multi-Streams for Android Malware Family Classification

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A Comprehensive Analysis of Today’s Malware and Its Distribution Network: Common Adversary Strategies and Implications

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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network

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Android Ransomware Detection using Machine Learning Techniques: A Comparative Analysis on GPU and CPU

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Identifying Android Malware Families Using Android-Oriented Metrics

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Assessing the Efficacy of Machine Learning and Deep Learning in the Field of Cyber Security

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RGB-based Android Malware Detection and Classification Using Convolutional Neural Network

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Android Malware Evasion Framework For Auditing Anti-Malware Resistance Against Various Obfuscation Technique And Dynamic Code Loading

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Peer Review #1 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.522v0.1/reviews/2openalex-snapshot

Peer Review #2 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.522v0.2/reviews/2openalex-snapshot

Peer Review #2 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.522v0.2/reviews/3openalex-snapshot

Peer Review #3 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.522v0.3/reviews/1openalex-snapshot

Peer Review #1 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.3)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.522v0.3/reviews/2openalex-snapshot

Peer Review #2 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.3)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.522v0.3/reviews/3openalex-snapshot

Peer Review #3 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.3)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.2616openalex-snapshot

Machine learning models and dimensionality reduction for improving the Android malware detection

inferred← citeshttps://doi.org/10.7717/peerj-cs.522openalex-snapshot

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

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.