Learning Filters with Certainty
For network systems using hash-based data structures, this work introduces a way to leverage uncertainty information from Counting Bloom Filters to enhance ML-integrated architectures.
The paper argues that the certainty of positive membership indications in Counting Bloom Filters can be exploited to improve architectures combining Bloom Filters with machine learning models, demonstrating that this certainty signal provides useful information beyond binary indications.
Hash-based data structures such as Bloom filters are widely used in network systems for tasks including caching, anomaly detection, and machine learning pipelines. They typically provide binary indications of whether an element belongs to a set of interest, e.g., the contents of a cache. When uncertainty arises due to hash collisions, a positive indication is returned to avoid false negatives. We argue that the certainty associated with such indications can itself be useful information. This work focuses on Counting Bloom Filters (CBFs), a Bloom-filter variant that maintains counters rather than bits. Besides supporting insertions and deletions, these counters provide additional information that can be used to estimate the certainty of positive membership indications. We show how this certainty signal can be exploited in architectures that combine Bloom Filters with machine learning (ML) models.