Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Design and Implementation of Machine Learning-based Blockchain DApp System

머신러닝 기반 블록체인 DApp 시스템 설계 및 구현

  • 이형우 (한신대학교 컴퓨터공학부) ;
  • 이한성 (한신대학교 대학원 컴퓨터공학과)
  • Received : 2020.10.14
  • Accepted : 2020.11.30
  • Published : 2020.12.31
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Abstract

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

본 논문에서는 지속적으로 급증하고 있는 안드로이드 악성 앱을 자동적으로 판별하기 위해 머신러닝 기법을 적용하여 프라이빗 블록체인을 토대로 웹 기반 DApp 시스템을 개발하였다. 공인 실험 데이터를 대상으로 안드로이드 악성 앱 판별에 96.2587% 정확도를 제공하는 최적의 머신러닝 모델을 선정하였고, 안드로이드 악성 앱에 대한 자동 판별 결과를 Hyperledger Fabric 블록체인 시스템 내에 자동적으로 기록/관리하였다. 또한 적법한 권한이 부여된 사용자만이 블록체인 시스템을 이용할 수 있도록 웹 기반의 DApp 시스템을 개발하였다. 따라서 본 논문에서 제시한 머신러닝 기반 안드로이드 악성 앱 판별 블록체인 DApp 시스템 개발을 통해 안드로이드 모바일 앱 이용 환경에서의 보안성을 더욱 향상시킬 수 있으며, 향후에 일반적인 범용 데이터를 대상으로 머신러닝과 블록체인을 결합한 보안 서비스로 발전시킬 수 있을 것으로 기대된다.

Keywords

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