# Regression-aware Continual Learning for Android Malware Detection

arXiv: https://arxiv.org/abs/2507.18313 · HTML: https://mlsanity.com/paper/2507.18313/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016)
- [Reinforced Continual Learning](https://mlsanity.com/paper/1805.12369/) (2018)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [Positive-Congruent Training: Towards Regression-Free Model Updates](https://mlsanity.com/paper/2011.09161/) (2020)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [Avalanche: A PyTorch Library for Deep Continual Learning](https://mlsanity.com/paper/2302.01766/) (2023)
- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models](https://mlsanity.com/paper/1612.04433/) (2016)
- [On the Limitations of Continual Learning for Malware Classification](https://mlsanity.com/paper/2208.06568/) (2022)
- [MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification](https://mlsanity.com/paper/2501.01110/) (2025)
- [ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training](https://mlsanity.com/paper/2205.06265/) (2022)
- [Backward Compatibility During Data Updates by Weight Interpolation](https://mlsanity.com/paper/2301.10546/) (2023)
- [Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion](https://mlsanity.com/paper/2302.02080/) (2023)
- [LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis](https://mlsanity.com/paper/2505.18551/) (2025)
- [Robust Machine Learning for Malware Detection over Time](https://mlsanity.com/paper/2208.04838/) (2022)
- [Unraveling the Key of Machine Learning-based Android Malware Detection](https://mlsanity.com/paper/2402.02953/) (2024)
- [Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates](https://mlsanity.com/paper/2402.17390/) (2024)

## Built on by (most cited first, 2 of 2)

- [DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection](https://mlsanity.com/paper/2510.12310/) (2025)
- [Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection](https://mlsanity.com/paper/2605.24294/) (2026)

## Abstract (arXiv)

Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated. With antivirus vendors processing hundreds of thousands of new samples daily, datasets can grow to billions of examples, making full retraining impractical. Continual learning (CL) has emerged as a scalable alternative, enabling incremental updates without full data access while mitigating catastrophic forgetting. In this work, we analyze a critical yet overlooked issue in this context: security regression. Unlike forgetting, which manifests as a drop in average performance on previously seen data, security regression captures harmful sample-level prediction changes, e.g., malware samples that were correctly detected before an update but evade detection afterward. This poses serious risks in security-critical applications, as the silent reintroduction of previously detected threats may undermine users' trust in the update process, leading them to perceive a regression in security even if the average model performance has actually improved. We first formalize and quantify security regression in CL-based malware detectors, revealing that up to 3-6% of malware experience it after model updates. We then address this issue by introducing a regression-aware framework to the CL setting. Specifically, we instantiate it via Positive Congruent Training (PCT), showing seamless integration with any prior CL strategy. Experiments on the ELSA, Tesseract, and AZ-Class datasets show that our method effectively halves regression across different CL scenarios while maintaining strong detection performance over time.