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Android based malware detection using a multifeature collaborative decision fusion approach

https://doi.org/10.1016/j.neucom.2014.10.004

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What this work says

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

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A Mathematical Theory of Communication

inferredcites →https://doi.org/10.1006/inco.1995.1136openalex-snapshot

Boosting a Weak Learning Algorithm by Majority

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/10.1007/978-3-642-33018-6_30openalex-snapshot

PUMA: Permission Usage to Detect Malware in Android

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

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

inferredcites →https://doi.org/10.1007/s10851-011-0304-0openalex-snapshot

One-Class Support Vector Ensembles for Image Segmentation and Classification

inferredcites →https://doi.org/10.1016/b978-1-55860-247-2.50035-8openalex-snapshot

Induction of One-Level Decision Trees

inferredcites →https://doi.org/10.1016/j.inffus.2013.04.006openalex-snapshot

A survey of multiple classifier systems as hybrid systems

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/10.1016/j.ins.2013.12.019openalex-snapshot

Clustering-based ensembles for one-class classification

inferredcites →https://doi.org/10.1016/j.ins.2013.12.053openalex-snapshot

Empowering difficult classes with a similarity-based aggregation in multi-class classification problems

inferredcites →https://doi.org/10.1016/j.neucom.2013.01.052openalex-snapshot

Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1023/a:1022643204877openalex-snapshot

Induction of Decision Trees

inferredcites →https://doi.org/10.1080/01969722.2013.803889openalex-snapshot

MAMA: MANIFEST ANALYSIS FOR MALWARE DETECTION IN ANDROID

inferredcites →https://doi.org/10.1109/eisic.2012.34openalex-snapshot

A Machine Learning Approach to Android Malware Detection

inferredcites →https://doi.org/10.1109/icc.2009.5199486openalex-snapshot

Static Analysis of Executables for Collaborative Malware Detection on Android

inferredcites →https://doi.org/10.1109/joe.2010.2101235openalex-snapshot

Multiview, Broadband Acoustic Classification of Marine Fish: A Machine Learning Framework and Comparative Analysis

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

Dissecting Android Malware: Characterization and Evolution

inferredcites →https://doi.org/10.1109/tifs.2013.2242890openalex-snapshot

SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

inferredcites →https://doi.org/10.1109/tits.2008.922970openalex-snapshot

Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video

inferredcites →https://doi.org/10.1142/s0129065714300071openalex-snapshot

IMPROVED ADAPTIVE SPLITTING AND SELECTION: THE HYBRID TRAINING METHOD OF A CLASSIFIER BASED ON A FEATURE SPACE PARTITIONING

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On lightweight mobile phone application certification

inferredcites →https://doi.org/10.1145/1656274.1656278openalex-snapshot

The WEKA data mining software

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

Crowdroid

inferredcites →https://doi.org/10.1145/2046707.2046779openalex-snapshot

Android permissions demystified

inferredcites →https://doi.org/10.1145/2351676.2351722openalex-snapshot

Automatically securing permission-based software by reducing the attack surface: an application to Android

inferredcites →https://doi.org/10.1145/382912.382914openalex-snapshot

A framework for constructing features and models for intrusion detection systems

inferredcites →https://doi.org/10.1162/089976699300016007openalex-snapshot

Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms

What points at this work

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

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Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification

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A Team Is Superior to an Individual

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Feature-Based Semi-supervised Learning to Detect Malware from Android

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Android Botnet Detection Based on Network Analysis Using Machine Learning Algorithm

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Exploring Feature Extraction and ELM in Malware Detection for Android Devices

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Multimodal Imaging Classification Based on Mixture Model Networks

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MLDroid—framework for Android malware detection using machine learning techniques

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Network-based detection of Android malicious apps

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Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection

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S3Feature: A static sensitive subgraph-based feature for android malware detection

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Malicious application detection in android — A systematic literature review

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PHIGrader: Evaluating the effectiveness of Manifest file components in Android malware detection using Multi Criteria Decision Making techniques

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SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

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

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/10.7287/peerj-cs.907v0.1/reviews/1openalex-snapshot

Peer Review #1 of "The rise of obfuscated Android malware and impacts on detection methods (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.907v0.1/reviews/2openalex-snapshot

Peer Review #2 of "The rise of obfuscated Android malware and impacts on detection methods (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.907v0.2/reviews/1openalex-snapshot

Peer Review #1 of "The rise of obfuscated Android malware and impacts on detection methods (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.907v0.2/reviews/2openalex-snapshot

Peer Review #2 of "The rise of obfuscated Android malware and impacts on detection methods (v0.2)"

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)

inferred← citeshttps://doi.org/10.7717/peerj-cs.907openalex-snapshot

The rise of obfuscated Android malware and impacts on detection methods

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.