# Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

arXiv: https://arxiv.org/abs/2010.12080 · HTML: https://mlsanity.com/paper/2010.12080/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [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)
- [Exact Acceleration of K-Means++ and K-Means|](https://mlsanity.com/paper/2105.02936/) (2021)
- [Cybersecurity is the True Frontier for Generative AI Success or Failure](https://mlsanity.com/paper/2606.28929/) (2026)
- [On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques](https://mlsanity.com/paper/2206.06256/) (2022)

## Abstract (arXiv)

False positives (FPs) have been an issue of extreme importance for anti-virus (AV) systems for decades. As more security vendors turn to machine learning, alert deluge has hit critical mass with over 20% of all alerts resulting in FPs and, in some organizations, the number reaches half of all alerts. This increase has resulted in fatigue, frustration, and, worst of all, neglect from security workers on SOC teams. A foundational cause for FPs is that vendors must build one global system to try and satisfy all customers, but have no method to adjust to individual local environments. This leads to outrageous, albeit technically correct, characterization of their platforms being 99.9% effective. Once these systems are deployed the idiosyncrasies of individual, local environments expose blind spots that lead to FPs and uncertainty. We propose a strategy for fixing false positives in production after a model has already been deployed. For too long the industry has tried to combat these problems with inefficient, and at times, dangerous allowlist techniques and excessive model retraining which is no longer enough. We propose using a technique called passive-aggressive learning to alter a malware detection model to an individual's environment, eliminating false positives without sharing any customer sensitive information. We will show how to use passive-aggressive learning to solve a collection of notoriously difficult false positives from a production environment without compromising the malware model's accuracy, reducing the total number of FP alerts by an average of 23x.