악성 안드로이드 앱 탐지를 위한 개선된 특성 선택 모델
Advanced Feature Selection Method on Android Malware Detection by Machine Learning
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초록
2018년 시만텍 보고서에 따르면, 모바일 환경에서 변종 악성 앱은 전년도 대비 54% 증가하였고, 매일 24,000개의 악성 앱이 차단되고 있다. 최근 연구에서는 기존 악성 앱 분석 기술의 사용 한계를 파악하고, 신ㆍ변종 악성앱을 탐지하기 위하여 기계학습을 통한 악성 앱 탐지 기법이 연구되고 있다. 하지만, 기계학습을 적용하는 경우에도악성 앱의 특성을 적절하게 선택하여 학습하지 못하면 올바른 결과를 보일 수 없다. 본 연구에서는 신ㆍ변종 악성앱의 특성을 찾아낼 수 있도록 개선된 특성 선택 방법을 적용하여 학습 모델의 정확도를 최고 98%까지 확인할 수있었다. 향후 연구를 통하여 정밀도, 재현율 등 특정 지표의 향상을 목표로 할 수 있다.
Abstract
According to Symantec's 2018 internet security threat report, The number of new mobile malware variants increased by54 percent in 2017, as compared to 2016. And last year, there were an average of 24,000 malicious mobile applicationsblocked each day. Existing signature-based technologies of malware detection have limitations. So, malware detectiontechnique through machine learning is being researched to detect malware variant. However, even in the case of applyingmachine learning, if the proper features of the malware are not properly selected, the machine learning cannot be showncorrectly. We are focusing on feature selection method to find the features of malware variant in this research.
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