Transformer-Boosted Anomaly Detection with Fuzzy Hashes. (arXiv:2208.11367v2 [cs.CR] UPDATED)

Fuzzy hashes are an important tool in digital forensics and are used in
approximate matching to determine the similarity between digital artifacts.
They translate the byte code of files into computable strings, which makes them
particularly interesting for intelligent machine processing. In this work, we
propose deep learning approximate matching (DLAM), which achieves much higher
accuracy in detecting anomalies in fuzzy hashes than conventional approaches.
In addition to the well-known application for clustering malware, we show that
fuzzy hashes and …

anomaly detection detection hashes