# Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection

arXiv: https://arxiv.org/abs/2508.09652 · HTML: https://mlsanity.com/paper/2508.09652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [Dos and Don'ts of Machine Learning in Computer Security](https://mlsanity.com/paper/2010.09470/) (2020)
- [Functionality-preserving Black-box Optimization of Adversarial Windows Malware](https://mlsanity.com/paper/2003.13526/) (2020)
- [SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection](https://mlsanity.com/paper/2012.07634/) (2020)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries](https://mlsanity.com/paper/1901.03583/) (2019)
- [Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection](https://mlsanity.com/paper/2008.07125/) (2020)
- [Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations](https://mlsanity.com/paper/2208.12248/) (2022)
- [SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines](https://mlsanity.com/paper/2405.14478/) (2024)
- [ModSec-Learn: Boosting ModSecurity with Machine Learning](https://mlsanity.com/paper/2406.13547/) (2024)

## Built on by (most cited first, 1 of 1)

- [Living Off the LLM: How LLMs Will Change Adversary Tactics](https://mlsanity.com/paper/2510.11398/) (2025)

## Abstract (arXiv)

Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples, carefully crafted programs designed to evade detection. Hence, in this work we investigate the influence that signature-based detection exerts on model training, when they are included inside the training pipeline. Specifically, we compare models trained on a comprehensive dataset with an AI system whose machine learning component is trained solely on samples not already flagged by signatures. Our results demonstrate improved robustness to both adversarial EXEmples and temporal data drift, although this comes at the cost of a fixed lower bound on false positives, driven by suboptimal rule selection. We conclude by discussing these limitations and outlining how future research could extend AI-based malware detection to include dynamic analysis, thereby further enhancing system resilience.