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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

머신러닝 기반 블록체인 DApp 시스템 설계 및 구현

Design and Implementation of Machine Learning-based Blockchain DApp System

한국사물인터넷학회논문지 / Journal of The Korea Internet of Things Society, (P)2466-0078;
2020, v.6 no.4, pp.65-72
https://doi.org/https://doi.org/10.20465/kiots.2020.6.4.065
이형우 (한신대학교)
이한성 (한신대학교)
  • 다운로드 수
  • 조회수

초록

본 논문에서는 지속적으로 급증하고 있는 안드로이드 악성 앱을 자동적으로 판별하기 위해 머신러닝 기법을적용하여 프라이빗 블록체인을 토대로 웹 기반 DApp 시스템을 개발하였다. 공인 실험 데이터를 대상으로 안드로이드악성 앱 판별에 96.2587% 정확도를 제공하는 최적의 머신러닝 모델을 선정하였고, 안드로이드 악성 앱에 대한 자동판별 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 서비스로발전시킬 수 있을 것으로 기대된다.

keywords
머신러닝, 블록체인, DApp 시스템, 안드로이드, 악성 앱 탐지, 모바일 보안, Machine Learning, Blockchain, DApp System, Android System, Malicious App Detection, Mobile Security

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.

keywords
머신러닝, 블록체인, DApp 시스템, 안드로이드, 악성 앱 탐지, 모바일 보안, Machine Learning, Blockchain, DApp System, Android System, Malicious App Detection, Mobile Security

참고문헌

1.

SAS Institute, “Machine Learning: What it is & why it matters”, https://www.sas.com/en_us/insights/analytics/machin e-learning.html (November 1, 2020)

2.

H. Lee, S. Chung, E. Choi, “A Case Study on Machine Learning Applications and Performance Improvement in Learning Algorithm,” Journal of Digital Convergence, Vol.14, No.2 pp.245-258, 2016.

3.

Machine Learning for Malware Detection, Kasperskey Lab, Edward Raff, Malware Detection by Eating a Whole EXE, NVIDIA, 2017.

4.

The Linux Foundation Project, Hyperledger Project URL: https://www.hyperledger.org

5.

Security Technology Research Team, “Introduction of malicious code detection research by foreign companies using machine learning,” Financial Security Institute Technical Report, 2018-029, 2018, 4, http://www.fsec.or.kr/common/proc/fsec/bbs/42/file DownLoad/1520.do

6.

Dong-Hyeok Park, Eui-Jung Myeong, Joobeom Yun, "Efficient Detection of Android Mutant Malwares Using the DEX file", Korea Institute Of Information Security And Cryptology, Vol.26, No.4, pp.895-902. 2016.

7.

Da-Hye Kim, Myeon-ggeon Lee, Min-Su Song, Seong-je Cho, "Machine Learning based Android Malware Detection using Gray Scale Images", KOREA INFORMATION SCIENCE SOCIETY, 1245-1247. 2018.

8.

S.J Cho, “Latest Android malicious app trends and detection techniques,” iitp technical report, 2019, 9

9.

Symantec. Internet Security Threat Report. Volume23. March 2018. https://docs.broadcom.com/doc/istr-23-2018-en.

10.

Victor Chebyshev. Mobile malware evolution 2019. February 25, 2020. http://securelist.com/mobile-malware-evolution-2019/96280/.

11.

Androguard. https://github.com/androguard/androguard.

12.

scikit-learn. https://scikit-learn.org/.

13.

S.M.Hwang and H.W.Lee, "Identification of Counterfeit Android Malware Apps using Hyperledger Fabric Blockchain," Journal of Internet Computing and Services, Vol.20, No.2, pp.61-68, 2019. DOI:10.7472/jksii.2019.20.2.61.

14.

H.S.Lee and H.W.Lee, "Consortium Blockchain based Forgery Android APK Discrimination DApp using Hyperledger Composer," Journal of Internet Computing and Services, Vol.20, No.5, pp.9-18, 2019. DOI: 10.7472/jksii.2019.20.5.9.

15.

H.S.Lee and H.W.Lee, "Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps,“ Journal of Korea Internet on Things Society, Vol.6, No.2, pp.1-10, 2020. DOI: 10.20465/KIOTS,2020.6.2.001.

한국사물인터넷학회논문지