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http://dx.doi.org/10.33778/kcsa.2020.20.3.053

Correlation Analysis of Dataset Size and Accuracy of the CNN-based Malware Detection Algorithm  

Choi, Dong Jun (중앙대학교/융합보안학과)
Lee, Jae Woo (중앙대학교/산업보안학과)
Publication Information
Convergence Security Journal / v.20, no.3, 2020 , pp. 53-60 More about this Journal
Abstract
At the present stage of the fourth industrial revolution, machine learning and artificial intelligence technologies are rapidly developing, and there is a movement to apply machine learning technology in the security field. Malicious code, including new and transformed, generates an average of 390,000 a day worldwide. Statistics show that security companies ignore or miss 31 percent of alarms. As many malicious codes are generated, it is becoming difficult for humans to detect all malicious codes. As a result, research on the detection of malware and network intrusion events through machine learning is being actively conducted in academia and industry. In international conferences and journals, research on security data analysis using deep learning, a field of machine learning, is presented. have. However, these papers focus on detection accuracy and modify several parameters to improve detection accuracy but do not consider the ratio of dataset. Therefore, this paper aims to reduce the cost and resources of many machine learning research by finding the ratio of dataset that can derive the highest detection accuracy in CNN Mobile net-based malware detection model.
Keywords
CNN Mobile Net; Malware Detection Algoritm; Machine Learning; Security Data Analysis; Network Event;
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