mlsanityData to 14 Oct 2026

MORPH: Towards Automated Concept Drift Adaptation for Malware Detection

arXiv 2401.12790 ↗First posted 23 Jan 2024cs.LGcited by 8

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Abstract (arXiv)

Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical. While prior research in malware concept drift adaptation has primarily focused on active learning, which involves selecting representative samples to update the model, self-training has emerged as a promising approach to mitigate concept drift. Self-training involves retraining the model using pseudo labels to adapt to shifting data distributions. In this research, we propose MORPH -- an effective pseudo-label-based concept drift adaptation method specifically designed for neural networks. Through extensive experimental analysis of Android and Windows malware datasets, we demonstrate the efficacy of our approach in mitigating the impact of concept drift. Our method offers the advantage of reducing annotation efforts when combined with active learning. Furthermore, our method significantly improves over existing works in automated concept drift adaptation for malware detection.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 19 of its 51 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

19 of 51 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
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  5. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
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  11. 2018Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learningcited by 61
  12. 2020Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Driftcited by 53
7 more
  1. 2023Continuous Learning for Android Malware Detectioncited by 33
  2. 2022Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTIcited by 27
  3. 2019On Training Robust PDF Malware Classifierscited by 19
  4. 2022On the Limitations of Continual Learning for Malware Classificationcited by 12
  5. 2022CyNER: A Python Library for Cybersecurity Named Entity Recognitioncited by 6
  6. 2022Self-Supervised Vision Transformers for Malware Detectioncited by 6
  7. 2021TINKER: A framework for Open source Cyberthreat Intelligencecited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 3.

8 papers.

  1. 2025Understanding Concept Drift with Deprecated Permissions in Android Malware Detectioncited by 2
  2. 2025Empirical Evaluation of Concept Drift in ML-Based Android Malware Detectioncited by 2
  3. 2024R+R: Revisiting Static Feature-Based Android Malware Detection using Machine Learningcited by 1
  4. 2025MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptationcited by 1
  5. 2025ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detectioncited by 1
  6. 2026Label-efficient Training Updates for Malware Detection over Timecited by 0
  7. 2026Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detectioncited by 0
  8. 2026Concept Drift Detection and Adaptive Retraining of Malware Classification Modelscited by 0