Concept Drift Detection and Adaptive Retraining of Malware Classification Models

• URL: http://arxiv.org/abs/2608.13465v1
• Date: Thu, 13 Aug 2026 16:46:56 GMT
• Title: Concept Drift Detection and Adaptive Retraining of Malware Classification Models
• Abstract summary: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model.<n>In this chapter, we analyze two machine learning-based approaches to automated concept drift detection.<n>We find that all three concept drift detection techniques achieve classification accuracy comparable to periodic retraining.
• Score: 0.1957338076370071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are particularly susceptible to performance degradation caused by concept drift, as attackers constantly modify existing malware. In this chapter, we analyze two machine learning-based approaches to automated concept drift detection-a novel approach based on One-Class Support Vector Machines (OCSVM) and a previously-studied technique based on Minibatch K-Means (MK-Means). For comparison we also consider Maximum Mean Discrepancy (MMD), a statistical technique for detecting changes in multidimensional data. We conduct an extensive series of experiments comparing the effectiveness of four learning models, namely, Multilayer Perceptron, Random Forest, Support Vector Machines, and eXtreme Gradient Boosting. For each of these models, we consider three distinct scenarios: A static scenario where no model retraining occurs, a periodic scenario where models are constantly retrained irrespective of concept drift, and a drift-aware scenario where models are only retrained when concept drift is detected. Under the drift-aware scenario, we analyze the tradeoff between accuracy and training efficiency using Pareto Front analysis. We find that all three concept drift detection techniques achieve classification accuracy comparable to periodic retraining, while offering substantially greater efficiency in terms of the number of models that must be retrained. In addition, drift-aware retraining based on our OCSVM technique generally outperforms the MK-Means and MMD approaches. Overall, these results provide strong evidence that we can accurately detect concept drift in malware classification models.

Related papers

• DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation [50.584834483403675]
  Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone.<n>We demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface.<n>We introduce DriftScope, a prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of tokens whose visual concepts have shifted most between them.
  arXiv  Detail & Related papers  (2026-06-30T20:57:51Z)
• Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation [53.750389076941396]
  Diffusion-based visuomotor policies capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control.<n>We propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes.<n>Ada3Drift achieves state-of-the-art performance while requiring $10times$ fewer function evaluations than diffusion-based alternatives.
  arXiv  Detail & Related papers  (2026-03-12T14:36:53Z)
• ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection [0.8192907805418583]
  Adapting machine learning models to changing data distributions requires frequent updates.<n>We introduce texttADAPT, a novel pseudo-labeling semi-supervised algorithm for addressing concept drift.
  arXiv  Detail & Related papers  (2025-07-11T13:47:07Z)
• Cluster Analysis and Concept Drift Detection in Malware [1.3812010983144798]
  Concept drift refers to gradual or sudden changes in the properties of data that affect the accuracy of machine learning models.<n>We propose and analyze a clustering-based approach to detecting concept drift in the malware domain.
  arXiv  Detail & Related papers  (2025-02-19T22:42:30Z)
• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification [17.399454244765842]
  DREAM is a novel system that improves drift detection and establishes an explanatory adaptation process.<n>Our evaluation shows that DREAM effectively improves the drift detection accuracy and reduces the expert analysis effort in adaptation.
  arXiv  Detail & Related papers  (2024-05-07T07:55:45Z)
• Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models [9.035254826664273]
  Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its performance. In our proposed unsupervised drift detection method, we follow a two step process. Our first step involves encoding a sample of production data as the target distribution, and the model training data as the reference distribution. Our method also identifies the subset of production data that is the root cause of the drift. The models retrained using these identified high drift samples show improved performance on online customer experience quality metrics.
  arXiv  Detail & Related papers  (2023-09-07T16:45:42Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors [68.8204255655161]
  This paper compares four state-of-the-art algorithms in two real applications: gesture recognition based on accelerometer data and image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs.
  arXiv  Detail & Related papers  (2022-09-01T17:05:20Z)
• Autoregressive based Drift Detection Method [0.0]
  We propose a new concept drift detection method based on autoregressive models called ADDM. Our results show that this new concept drift detection method outperforms the state-of-the-art drift detection methods.
  arXiv  Detail & Related papers  (2022-03-09T14:36:16Z)
• Churn Reduction via Distillation [54.5952282395487]
  We show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines.
  arXiv  Detail & Related papers  (2021-06-04T18:03:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.