| Summary: | Every day, hundreds of thousands of new malicious files are created. Existing pattern-based antivirus solutions have difficulty detecting these new malicious files. Artificial intelligence (AI)–based malware detection has been proposed to solve the problem; however, it takes a long time. Similarity hash–based detection has also been proposed; however, it has a low detection rate. To solve these problems, we propose k-nearest-neighbor (kNN) classification for malware detection with a vantage-point (VP) tree using a similarity hash. When we use kNN classification, we reduce the detection time by 67% and increase the detection rate by 25%. With a VP tree using a similarity hash, we reduce the similarity-hash search time by 20%.
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