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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 FPsand, in some organizations, the number reaches half of all alerts. Thisincrease has resulted in fatigue, frustration, and, worst of all, neglect fromsecurity workers on SOC teams. A foundational cause for FPs is that vendorsmust build one global system to try and satisfy all customers, but have nomethod 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 modelhas already been deployed. For too long the industry has tried to combat theseproblems with inefficient, and at times, dangerous allowlist techniques andexcessive model retraining which is no longer enough. We propose using atechnique called passive-aggressive learning to alter a malware detection modelto an individual's environment, eliminating false positives without sharing anycustomer sensitive information. We will show how to use passive-aggressivelearning to solve a collection of notoriously difficult false positives from aproduction environment without compromising the malware model's accuracy,reducing the total number of FP alerts by an average of 23x.

Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors