Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020
Fuente:
arXiv
Saved in:
| Main Authors: | Duarte, Diego Ferreira, Bortoli, Andre Augusto |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Comparison of Multiple Classifiers for Android Malware Detection with Emphasis on Feature Insights Using CICMalDroid 2020 Dataset
by: Abedin, Md Min-Ha-Zul, et al.
Published: (2026)
by: Abedin, Md Min-Ha-Zul, et al.
Published: (2026)
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
by: Bostani, Hamid, et al.
Published: (2021)
by: Bostani, Hamid, et al.
Published: (2021)
The Impact of Train-Test Leakage on Machine Learning-based Android Malware Detection
by: Liu, Guojun, et al.
Published: (2024)
by: Liu, Guojun, et al.
Published: (2024)
AndroWasm: an Empirical Study on Android Malware Obfuscation through WebAssembly
by: Soi, Diego, et al.
Published: (2026)
by: Soi, Diego, et al.
Published: (2026)
Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)
by: Liu, Jiahao, et al.
Published: (2024)
CorrNetDroid: Android Malware Detector leveraging a Correlation-based Feature Selection for Network Traffic features
by: Sharma, Yash, et al.
Published: (2025)
by: Sharma, Yash, et al.
Published: (2025)
Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)
by: Sabbah, Ahmed, et al.
Published: (2025)
DroidTTP: Mapping Android Applications with TTP for Cyber Threat Intelligence
by: Arikkat, Dincy R, et al.
Published: (2025)
by: Arikkat, Dincy R, et al.
Published: (2025)
Benchmarking Android Malware Detection: Traditional vs. Deep Learning Models
by: Liu, Guojun, et al.
Published: (2025)
by: Liu, Guojun, et al.
Published: (2025)
Enhancing Android Malware Detection with Retrieval-Augmented Generation
by: S., Saraga, et al.
Published: (2025)
by: S., Saraga, et al.
Published: (2025)
Regression-aware Continual Learning for Android Malware Detection
by: Ghiani, Daniele, et al.
Published: (2025)
by: Ghiani, Daniele, et al.
Published: (2025)
Android Malware Detection: A Machine Leaning Approach
by: Abdulla, Hasan
Published: (2025)
by: Abdulla, Hasan
Published: (2025)
ActDroid: An active learning framework for Android malware detection
by: Muzaffar, Ali, et al.
Published: (2024)
by: Muzaffar, Ali, et al.
Published: (2024)
KeyDroid: A Large-Scale Analysis of Secure Key Storage in Android Apps
by: Blessing, Jenny, et al.
Published: (2025)
by: Blessing, Jenny, et al.
Published: (2025)
FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection
by: Song, Shiwen, et al.
Published: (2025)
by: Song, Shiwen, et al.
Published: (2025)
LLM-Generated Samples for Android Malware Detection
by: Rollinson, Nik, et al.
Published: (2025)
by: Rollinson, Nik, et al.
Published: (2025)
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
by: Lan, Tianwei, et al.
Published: (2025)
by: Lan, Tianwei, et al.
Published: (2025)
R+R: Revisiting Static Feature-Based Android Malware Detection using Machine Learning
by: Alam, Md Tanvirul, et al.
Published: (2024)
by: Alam, Md Tanvirul, et al.
Published: (2024)
Measuring and Explaining the Effects of Android App Transformations in Online Malware Detection
by: Meng, Guozhu, et al.
Published: (2025)
by: Meng, Guozhu, et al.
Published: (2025)
Multi-label Classification for Android Malware Based on Active Learning
by: Qiao, Qijing, et al.
Published: (2024)
by: Qiao, Qijing, et al.
Published: (2024)
Can you See me? On the Visibility of NOPs against Android Malware Detectors
by: Soi, Diego, et al.
Published: (2023)
by: Soi, Diego, et al.
Published: (2023)
Learning Temporal Invariance in Android Malware Detectors
by: Zheng, Xinran, et al.
Published: (2025)
by: Zheng, Xinran, et al.
Published: (2025)
Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
by: Chow, Theo, et al.
Published: (2025)
by: Chow, Theo, et al.
Published: (2025)
Explainable Android Malware Detection and Malicious Code Localization Using Graph Attention
by: Ipek, Merve Cigdem, et al.
Published: (2025)
by: Ipek, Merve Cigdem, et al.
Published: (2025)
XAI and Android Malware Models
by: Kulkarni, Maithili, et al.
Published: (2024)
by: Kulkarni, Maithili, et al.
Published: (2024)
Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
by: Fu, Annan, et al.
Published: (2026)
by: Fu, Annan, et al.
Published: (2026)
Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)
by: Sabbah, Ahmed, et al.
Published: (2025)
Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic
by: A, Saravanan, et al.
Published: (2026)
by: A, Saravanan, et al.
Published: (2026)
Feature-Centric Approaches to Android Malware Analysis: A Survey
by: Maganur, Shama, et al.
Published: (2025)
by: Maganur, Shama, et al.
Published: (2025)
MARD: A Multi-Agent Framework for Robust Android Malware Detection
by: Zeng, Xueying, et al.
Published: (2026)
by: Zeng, Xueying, et al.
Published: (2026)
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
by: Ranaweera, Nishavi, et al.
Published: (2025)
by: Ranaweera, Nishavi, et al.
Published: (2025)
Android Malware Detection Based on RGB Images and Multi-feature Fusion
by: Wang, Zhiqiang, et al.
Published: (2024)
by: Wang, Zhiqiang, et al.
Published: (2024)
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
by: He, Yiling, et al.
Published: (2024)
by: He, Yiling, et al.
Published: (2024)
Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid
by: Chaieb, Meryam, et al.
Published: (2024)
by: Chaieb, Meryam, et al.
Published: (2024)
Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification
by: Costa, Francisco, et al.
Published: (2025)
by: Costa, Francisco, et al.
Published: (2025)
DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
by: Trung, Doan Minh, et al.
Published: (2025)
by: Trung, Doan Minh, et al.
Published: (2025)
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
by: Gibert, Daniel
Published: (2024)
by: Gibert, Daniel
Published: (2024)
On Benchmarking Code LLMs for Android Malware Analysis
by: He, Yiling, et al.
Published: (2025)
by: He, Yiling, et al.
Published: (2025)
Detecting Android Malware by Visualizing App Behaviors from Multiple Complementary Views
by: Meng, Zhaoyi, et al.
Published: (2024)
by: Meng, Zhaoyi, et al.
Published: (2024)
Enhancing Android Malware Detection: The Influence of ChatGPT on Decision-centric Task
by: Li, Yao, et al.
Published: (2024)
by: Li, Yao, et al.
Published: (2024)
Similar Items
-
Comparison of Multiple Classifiers for Android Malware Detection with Emphasis on Feature Insights Using CICMalDroid 2020 Dataset
by: Abedin, Md Min-Ha-Zul, et al.
Published: (2026) -
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
by: Bostani, Hamid, et al.
Published: (2021) -
The Impact of Train-Test Leakage on Machine Learning-based Android Malware Detection
by: Liu, Guojun, et al.
Published: (2024) -
AndroWasm: an Empirical Study on Android Malware Obfuscation through WebAssembly
by: Soi, Diego, et al.
Published: (2026) -
Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)