# Cross-Domain Malware Detection via Probability-Level Fusion of Lightweight Gradient Boosting Models

arXiv: https://arxiv.org/abs/2509.00476 · HTML: https://mlsanity.com/paper/2509.00476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Microsoft Malware Classification Challenge](https://mlsanity.com/paper/1802.10135/) (2018)

## Abstract (arXiv)

The escalating sophistication of malware necessitates robust detection mechanisms that generalize across diverse data sources. Traditional single-dataset models struggle with cross-domain generalization and often incur high computational costs. This paper presents a novel, lightweight framework for malware detection that employs probability-level fusion across three distinct datasets: EMBER (static features), API Call Sequences (behavioral features), and CIC Obfuscated Memory (memory patterns). Our method trains individual LightGBM classifiers on each dataset, selects top predictive features to ensure efficiency, and fuses their prediction probabilities using optimized weights determined via grid search. Extensive experiments demonstrate that our fusion approach achieves a macro F1-score of 0.823 on a cross-domain validation set, significantly outperforming individual models and providing superior generalization. The framework maintains low computational overhead, making it suitable for real-time deployment, and all code and data are provided for full reproducibility.