Similarity Hash Index
Sunoh Choi, Youngsoo Kim, Jonghyun Kim · 2018
Hundreds of thousands of new malicious files are being created every day. Existing pattern-based vaccine engines cannot detect these new malicious files. To solve these problems, artificial intelligence based malicious file detection methods have been proposed. However, artificial intelligence based malicious file detection method has a disadvantage that takes long time because it requires dynamic analysis. We can use similarity hashes to solve these problems and find similar files. However, it also takes a long time to compare similarity hashes when there are many files. To solve this problem, this paper proposes a method to generate similarity hash index.