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With the development of the Internet, the number of cyber threats is continuously increasing and their techniques are also evolving for the purpose of attacking our crucial systems. Since attackers are able to easily make exploit codes, i.e., malware, using dedicated generation tools, the number of malware is rapidly increasing. However, it is not easy to analyze all of malware due to an extremely large number of malware. Because of this, many researchers have proposed the malware classification methods that aim to identify unforeseen malware from the well-known malware. The existing malware classification methods used malicious information obtained from the static and the dynamic malware analysis as the criterion of calculating the similarity between malwares. Also, most of them used API functions and their sequences that are divided into a certain length. Thus, the accuracy of the malware classification heavily depends on the length of divided API sequences. In this paper, we propose an extraction method of optimized API sequence length and combination that can be used for improving the performance of the malware classification.

A study on extraction of optimized API sequence length and combination for efficient malware classification