arXiv (All)
open access
When Label Noise Meets Class Imbalance: A Robust Framework for Android Malware Family Classification
Machine learning methods for Android malware family classification have achieved high accuracy, but their application is hindered by two major challenges. First, the widely used code obfuscation severely disrupts the automated labeling process and introduces substantial label noise into training dat
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection#224441
- Android Malware Detection#103112
- Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations#134497
- Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection#798585
- Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection#797172
Record · ID 1037518
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive