• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1019-1026
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• 2019

Due to limitations of existing methods for detecting newly introduced malware, the importance of the development of artificial intelligence vaccines arises. Existing artificial intelligence vaccines have a disadvantage that the accuracy of the detection rate is low because those vaccines do not scan all parts of the file. In this paper, we suggest an enhanced method for detecting malware which is composed of unique OP Code features in the malware files. Specifically, we tested the method with text datasets trained on Random Forest algorithm and with image datasets trained on the Inception V3 model. As a result, the highest accuracy of the detection rate was about 80%.