mlsanityData to 7 Oct 2026

Efficient Concept Drift Handling for Batch Android Malware Detection Models

arXiv 2309.09807 ↗First posted 18 Sep 2023cs.CR cs.AI cs.LGcited by 5

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Abstract (arXiv)

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 18 Sep 2023.

Builds on

13 of 41 references.

References · 13

  1. 2020Learning under Concept Drift: A Reviewcited by 470
  2. 2020A review on outlier/anomaly detection in time series datacited by 187
  3. 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
  4. 2015Characterizing Concept Driftcited by 93
  5. 2022From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectorscited by 82
  6. 2020Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Driftcited by 53
  7. 2023Continuous Learning for Android Malware Detectioncited by 33
  8. 2017Survey of Machine Learning Techniques for Malware Analysiscited by 31
  9. 2017Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)cited by 11
  10. 2017MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)cited by 11
  11. 2020Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Processcited by 7
  12. 2022Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learningcited by 6
1 more
  1. 2019Don't Pick the Cherry: An Evaluation Methodology for Android Malware Detection Methodscited by 2

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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2025Cluster Analysis and Concept Drift Detection in Malwarecited by 2
  2. 2025Understanding Concept Drift with Deprecated Permissions in Android Malware Detectioncited by 2
  3. 2025MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptationcited by 1
  4. 2025DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detectioncited by 0
  5. 2026Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detectioncited by 0