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A Method for Class-Imbalance Learning in Android Malware Detection

https://doi.org/10.3390/electronics10243124

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What this work says

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

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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

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Data Mining for Imbalanced Datasets: An Overview

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Robust hybrid data-level sampling approach to handle imbalanced data during classification

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Simulated annealing based undersampling (SAUS): a hybrid multi-objective optimization method to tackle class imbalance

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Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins

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A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining

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Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study

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UFFDFR: Undersampling framework with denoising, fuzzy c-means clustering, and representative sample selection for imbalanced data classification

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Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification

inferredcites →https://doi.org/10.1016/j.neucom.2017.07.030openalex-snapshot

DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

inferredcites →https://doi.org/10.1016/j.neucom.2018.10.056openalex-snapshot

Class imbalance learning using UnderBagging based kernelized extreme learning machine

inferredcites →https://doi.org/10.1016/j.neucom.2020.08.060openalex-snapshot

EHSO: Evolutionary Hybrid Sampling in overlapping scenarios for imbalanced learning

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A systematic study of the class imbalance problem in convolutional neural networks

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A random forests quantile classifier for class imbalanced data

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A Review of Android Malware Detection Approaches Based on Machine Learning

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DroidPDF: The Obfuscation Resilient Packer Detection Framework for Android Apps

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Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

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A Comprehensive Analysis of the Android Permissions System

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Radius-SMOTE: A New Oversampling Technique of Minority Samples Based on Radius Distance for Learning From Imbalanced Data

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Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset

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A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

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Landmine detection improvement using one-class SVM for unbalanced data

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Cyber Threat Detection Using Machine Learning Techniques: A Performance Evaluation Perspective

inferredcites →https://doi.org/10.1109/icpr.2008.4761297openalex-snapshot

RUSBoost: Improving classification performance when training data is skewed

inferredcites →https://doi.org/10.1109/ijcnn.2008.4633969openalex-snapshot

ADASYN: Adaptive synthetic sampling approach for imbalanced learning

inferredcites →https://doi.org/10.1109/isdfs.2019.8757523openalex-snapshot

Overview of machine learning methods for Android malware identification

inferredcites →https://doi.org/10.1109/jsen.2020.3038660openalex-snapshot

Feature Extraction, Selection, and K-Nearest Neighbors Algorithm for Shark Behavior Classification Based on Imbalanced Dataset

inferredcites →https://doi.org/10.1109/tcbb.2019.2921961openalex-snapshot

MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification

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Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis

inferredcites →https://doi.org/10.1109/tsmcb.2008.2007853openalex-snapshot

Exploratory Undersampling for Class-Imbalance Learning

inferredcites →https://doi.org/10.1145/1007730.1007735openalex-snapshot

A study of the behavior of several methods for balancing machine learning training data

inferredcites →https://doi.org/10.14722/ndss.2018.23296openalex-snapshot

Things You May Not Know About Android (Un)Packers: A Systematic Study based on Whole-System Emulation

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Trainable Undersampling for Class-Imbalance Learning

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Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction

inferredcites →https://doi.org/10.31209/2018.100000051openalex-snapshot

Protecting Android Applications with Multiple DEX Files against Static Reverse Engineering Attacks

inferredcites →https://doi.org/10.3390/electronics9060942openalex-snapshot

Android Malware Family Classification and Analysis: Current Status and Future Directions

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Experimental Investigation of the Dynamic Tensile Properties of Naturally Saturated Rocks Using the Coupled Static–Dynamic Flattened Brazilian Disc Method

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Blockchain Based Detection of Android Malware using Ranked Permissions

What points at this work

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

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Digitization of Forensic Engineering: Overview, Perspectives and New Challenges

inferred← citeshttps://doi.org/10.1007/978-3-031-83157-7_3openalex-snapshot

Comparing Balancing Techniques for Malware Classification

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SDB-RGSO: Swarm-Based Data Balancing and Randomized Grid Search Optimization for IoT NetFlow Malware Detection with Ensemble Machine Learning Model

inferred← citeshttps://doi.org/10.1007/s00521-026-12090-3openalex-snapshot

Malware Clustering Using A Deep Generative Model

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A comprehensive review on permissions-based Android malware detection

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ARdetector: android ransomware detection framework

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Hybrid Machine Learning-Based Multi-Stage Framework for Detection of Credit Card Anomalies and Fraud

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Classification of Malware Families Under Extreme Class Imbalance Using Non-Negative Tensor Decomposition

inferred← citeshttps://doi.org/10.1109/iscs61804.2024.10581231openalex-snapshot

A Review on Ransomware Detection in Android Using Feature Selection and Machine Learning

inferred← citeshttps://doi.org/10.1109/rivf68649.2025.11365070openalex-snapshot

Imbalance-Robust Class-Aligned Vector Encoders for Android Malware Detection

inferred← citeshttps://doi.org/10.24138/jcomss-2023-0133openalex-snapshot

SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

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Backdoor Malware Detection in Industrial IoT Using Machine Learning

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A Resilient Deep Learning Framework for Mobile Malware Detection: From Architecture to Deployment

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A SURVEY ON DEEP LEARNING TECHNIQUES FOR DARKNET TRAFFIC MALWARE DETECTION

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