@inproceedings{2c8f0c335791461d824508a11e9613c1,
title = "Similarity Hash Index",
abstract = "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.",
keywords = "index, local sensitive hash, similarity hash",
author = "Sunoh Choi and Youngsoo Kim and Jonghyun Kim",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th International Conference on Information and Communication Technology Convergence, ICTC 2018 ; Conference date: 17-10-2018 Through 19-10-2018",
year = "2018",
month = nov,
day = "16",
doi = "10.1109/ICTC.2018.8539650",
language = "English",
series = "9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1298--1300",
booktitle = "9th International Conference on Information and Communication Technology Convergence",
}