  Recent work has proposed the Lempel-Ziv Jaccard Distance (LZJD) as a method
to measure the similarity between binary byte sequences for malware
classification. We propose and test LZJD's effectiveness as a similarity digest
hash for digital forensics. To do so we develop a high performance Java
implementation with the same command-line arguments as sdhash, making it easy
to integrate into existing workflows. Our testing shows that LZJD is effective
for this task, and significantly outperforms sdhash and ssdeep in its ability
to match related file fragments and files corrupted with random noise. In
addition, LZJD is up to 60x faster than sdhash at comparison time.
