Fuzzy hashing, also known as similarity hashing,[1] is a technique for detecting data that is similar, but not exactly the same, as other data. This is in contrast to cryptographic hash functions, which are designed to have significantly different hashes for even minor differences. Fuzzy hashing has been used to identify malware[2][3] and has potential for other applications, like data loss prevention and detecting multiple versions of code.[4][5]
NIST.SP.800-168
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Beyond Precision and Recall: Understanding Uses (and Misuses) of Similarity Hashes in Binary Analysis
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Forensic Malware Analysis: The Value of Fuzzy Hashing Algorithms in Identifying Similarities
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ssdeep
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tlsh
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