# TIF: Learning Temporal Invariance in Android Malware Detectors

arXiv: https://arxiv.org/abs/2502.05098 · HTML: https://mlsanity.com/paper/2502.05098/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020)
- [Environment Inference for Invariant Learning](https://mlsanity.com/paper/2010.07249/) (2020)
- [The Risks of Invariant Risk Minimization](https://mlsanity.com/paper/2010.05761/) (2020)
- [Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization](https://mlsanity.com/paper/2106.06607/) (2021)
- [On Calibration and Out-of-domain Generalization](https://mlsanity.com/paper/2102.10395/) (2021)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [Regularization for Deep Learning: A Taxonomy](https://mlsanity.com/paper/1710.10686/) (2017)
- [Heterogeneous Risk Minimization](https://mlsanity.com/paper/2105.03818/) (2021)
- [Episode-based Prototype Generating Network for Zero-Shot Learning](https://mlsanity.com/paper/1909.03360/) (2019)
- [Robustness to Spurious Correlations via Human Annotations](https://mlsanity.com/paper/2007.06661/) (2020)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [Learning with Mixture of Prototypes for Out-of-Distribution Detection](https://mlsanity.com/paper/2402.02653/) (2024)
- [Evaluating Explanation Methods for Deep Learning in Security](https://mlsanity.com/paper/1906.02108/) (2019)
- [Unshuffling Data for Improved Generalization](https://mlsanity.com/paper/2002.11894/) (2020)
- [Understanding and Improving Feature Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2304.11327/) (2023)
- [Rich Feature Construction for the Optimization-Generalization Dilemma](https://mlsanity.com/paper/2203.15516/) (2022)
- [Continuous Learning for Android Malware Detection](https://mlsanity.com/paper/2302.04332/) (2023)
- [Invariant Learning via Probability of Sufficient and Necessary Causes](https://mlsanity.com/paper/2309.12559/) (2023)
- [When Does Group Invariant Learning Survive Spurious Correlations?](https://mlsanity.com/paper/2206.14534/) (2022)
- [Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples](https://mlsanity.com/paper/2407.13918/) (2024)
- [Exploiting Code Symmetries for Learning Program Semantics](https://mlsanity.com/paper/2308.03312/) (2023)
- [Robust Machine Learning for Malware Detection over Time](https://mlsanity.com/paper/2208.04838/) (2022)
- [Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration](https://mlsanity.com/paper/2401.17541/) (2024)
- [Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid](https://mlsanity.com/paper/2405.03620/) (2024)

## Built on by (most cited first, 4 of 4)

- [LAMD: Context-driven Android Malware Detection and Classification with LLMs](https://mlsanity.com/paper/2502.13055/) (2025)
- [On the Reliability and Stability of Selective Methods in Malware Classification Tasks](https://mlsanity.com/paper/2505.22843/) (2025)
- [MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research](https://mlsanity.com/paper/2511.00342/) (2025)
- [Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning](https://mlsanity.com/paper/2607.09179/) (2026)

## Abstract (arXiv)

Learning-based Android malware detectors degrade over time due to natural distribution drift caused by malware variants and new families. This paper systematically investigates the challenges classifiers trained with empirical risk minimization (ERM) face against such distribution shifts and attributes their shortcomings to their inability to learn stable discriminative features. Invariant learning theory offers a promising solution by encouraging models to generate stable representations across environments that expose the instability of the training set. However, the lack of prior environment labels, the diversity of drift factors, and low-quality representations caused by diverse families make this task challenging. To address these issues, we propose TIF, the first temporal invariant training framework for malware detection, which aims to enhance the ability of detectors to learn stable representations across time. TIF organizes environments based on application observation dates to reveal temporal drift, integrating specialized multi-proxy contrastive learning and invariant gradient alignment to generate and align environments with high-quality, stable representations. TIF can be seamlessly integrated into any learning-based detector. Experiments on a decade-long dataset show that TIF excels, particularly in early deployment stages, addressing real-world needs and outperforming state-of-the-art methods.