How Cuckoo Filter Can Improve Existing Approximate Matching Techniques
In recent years, approximate matching algorithms havebecome an important component in digital forensic research and have been adopted in some other working areas as well. Currently there are several approaches, but sdhash and mrsh-v2 especially attract the attention of the community because of their good overall performance (runtime, compression and detection rates). Although both approaches have quite a different proceeding, their final output (the similarity digest) is very similar as both utilize Bloom filters. This data structure was presented in 1970 and thus has been used for a while. Recently, a new data structure was proposed which claimed to be faster and have a smaller memory footprint than Bloom filter – Cuckoo filter.
In this paper we analyze the feasibility of Cuckoo filter for approximate matching algorithms and present a prototype implementation called mrsh-cf which is based on a special version of mrsh-v2 called mrsh-net. We demonstrate that by using Cuckoo filter there is a runtime improvement of approximately 37 % and also a significantly better false positive rate. The memory footprint of mrsh-cf is 8 times smaller than mrsh-net, while the compression rate is twice than Bloom filter based fingerprint.
KeywordsApproximate matching Similarity hashing Bloom filter Cuckoo filter Fuzzy hashing Similarity hashing mrsh-v2 mrsh-net
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