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

arXiv: https://arxiv.org/abs/2604.22629 · HTML: https://mlsanity.com/paper/2604.22629/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [Continuous Learning for Android Malware Detection](https://mlsanity.com/paper/2302.04332/) (2023)
- [EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers](https://mlsanity.com/paper/2506.05074/) (2025)
- [Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification](https://mlsanity.com/paper/2405.04095/) (2024)
- [Fast & Furious: Modelling Malware Detection as Evolving Data Streams](https://mlsanity.com/paper/2205.12311/) (2022)
- [Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2409.13723/) (2024)
- [Detecting and Explaining Malware Family Evolution Using Rule-Based Drift Analysis](https://mlsanity.com/paper/2602.03489/) (2026)
- [Counteracting Concept Drift by Learning with Future Malware Predictions](https://mlsanity.com/paper/2404.09352/) (2024)

## Abstract (arXiv)

This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the EMBER2024 dataset, and drift is quantified by comparing extracted rule representations using feature importance, prediction agreement, activation stability, and coverage metrics. These metrics are correlated with both accuracy degradation and data distribution shift as complementary drift indicators. The approach is evaluated across six malware families using fixed-interval and clustering-based windowing in family-vs-benign and family-vs-family settings, and compared against RIPPER and Transcendent baselines. Results show that fixed two-month windowing with feature-level Pearson correlation is the most reliable configuration, being the only one where all family pairs produce positive drift-accuracy correlations. The methods are complementary - no single approach dominates across all pairs.