:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Guldemir, Numan Halit, Olukoya, Oluwafemi, Martínez-del-Rincón, Jesús
Format:	Preprint
Published:	2026
Subjects:	Cryptography and Security Machine Learning
Online Access:	https://arxiv.org/abs/2601.17907
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Addressing malware family concept drift with triplet autoencoder
by: Guldemir, Numan Halit, et al.
Published: (2025)

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

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
by: Kalný, Tomáš, et al.
Published: (2026)

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

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

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

LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
by: Haque, Md Ahsanul, 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)

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

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

MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning
by: Li, Jiliang, et al.
Published: (2024)

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

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

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

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

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

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

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

Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
by: Barbero, Federico, et al.
Published: (2020)

Thwarting Cybersecurity Attacks with Explainable Concept Drift
by: Shaer, Ibrahim, et al.
Published: (2024)

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

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

Assessing Cyclostationary Malware Detection via Feature Selection and Classification
by: Nkongolo, Mike
Published: (2023)

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
by: Gond, Bishwajit Prasad, et al.
Published: (2025)

Image-Based Malware Classification Using QR and Aztec Codes
by: Khadilkar, Atharva, 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)

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

Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware
by: Karunanayake, Ishan, et al.
Published: (2024)

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

TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification
by: Borah, Parthajit, et al.
Published: (2026)

Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification
by: Hossain, Jakir, et al.
Published: (2025)

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification
by: Zhu, Jinting, et al.
Published: (2024)

Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams
by: Gower-Winter, Brandon, et al.
Published: (2024)

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification
by: Khan, Naseem, et al.
Published: (2025)

System Calls for Malware Detection and Classification: Methodologies and Applications
by: Gond, Bishwajit Prasad, et al.
Published: (2025)

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

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
by: Pulido-Cortázar, Daniel, et al.
Published: (2025)

Quantifying the Generalization Gap: A New Benchmark for Out-of-Distribution Graph-Based Android Malware Classification
by: Tran, Ngoc N., et al.
Published: (2025)

Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
by: He, Yiling, et al.
Published: (2024)