Cover Image

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kalný, Tomáš, Jureček, Martin, Stamp, Mark
Format:	Preprint
Published:	2026
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Online Clustering of Known and Emerging Malware Families
by: Jurečková, Olha, et al.
Published: (2024)

RawMal-TF: Raw Malware Dataset Labeled by Type and Family
by: Bálik, David, et al.
Published: (2025)

Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)

A Comparison of Adversarial Learning Techniques for Malware Detection
by: Louthánová, Pavla, et al.
Published: (2023)

Detecting and Explaining Malware Family Evolution Using Rule-Based Drift Analysis
by: Jurečková, Olha, et al.
Published: (2026)

Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
by: Košťál, David, et al.
Published: (2026)

Image-Based Malware Classification Using QR and Aztec Codes
by: Khadilkar, Atharva, et al.
Published: (2024)

Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
by: Hrdonka, Lukáš, et al.
Published: (2026)

Multimodal Techniques for Malware Classification
by: Jiang, Jonathan, et al.
Published: (2025)

XAI and Android Malware Models
by: Kulkarni, Maithili, et al.
Published: (2024)

Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
by: Dolejš, Jan, et al.
Published: (2026)

Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
by: Jurečková, Olha, et al.
Published: (2026)

FARM: Few-shot Adaptive Malware Family Classification under Concept Drift
by: Guldemir, Numan Halit, et al.
Published: (2026)

Creating Valid Adversarial Examples of Malware
by: Kozák, Matouš, et al.
Published: (2023)

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
by: Roh, Eunjin, et al.
Published: (2025)

ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection
by: Alam, Md Tanvirul, et al.
Published: (2025)

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)

A Comparison of Selected Image Transformation Techniques for Malware Classification
by: Agrawal, Rishit, et al.
Published: (2025)

Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2026)

LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
by: Haque, Md Ahsanul, et al.
Published: (2025)

Optimized Deep Learning Models for Malware Detection under Concept Drift
by: Maillet, William, et al.
Published: (2023)

Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
by: Kozák, Matouš, et al.
Published: (2025)

Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2026)

On the Effectiveness of Adversarial Training on Malware Classifiers
by: Bostani, Hamid, et al.
Published: (2024)

Robustness of Selected Learning Models under Label-Flipping Attack
by: Bhargava, Sarvagya, et al.
Published: (2025)

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)

McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
by: Kamol, Md Mahmuduzzaman, et al.
Published: (2026)

Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
by: Gupta, Siddhant, et al.
Published: (2024)

Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers
by: Ajayi, Bamidele, et al.
Published: (2025)

Adversarial Patterns: Building Robust Android Malware Classifiers
by: Bhusal, Dipkamal, et al.
Published: (2022)

Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
by: Gond, Bishwajit Prasad, et al.
Published: (2025)

Concept Drift Detection using Ensemble of Integrally Private Models
by: Varshney, Ayush K., et al.
Published: (2024)

SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
by: Amalapuram, Suresh Kumar, et al.
Published: (2026)

Temporal Analysis of Adversarial Attacks in Federated Learning
by: Mapakshi, Rohit, et al.
Published: (2025)

A Survey of Malware Detection Using Deep Learning
by: Bensaoud, Ahmed, et al.
Published: (2024)

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
by: Joyce, Robert J., et al.
Published: (2025)

Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift
by: Carnier, Rodrigo Matos, et al.
Published: (2025)

Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples
by: Li, Adrian Shuai, et al.
Published: (2024)