Earticle

초록

영어

In an effort to raise funds, North Korea now performs hacking assaults against the world's financial institutions. More specifically, the North Korean hackers demand money to decrypt the files they created, and since these transactions are handled anonymously, it is difficult to identify them. Therefore, this research uses the BitcoinHeist dataset to identify cryptocurrency-related ransomware. We construct the experiment with two distinct steps: classification and anomaly detection. The XG boosting technique achieved a 100% accuracy score in the first experiment. Even though anomaly detection methods were used in the second trial for detection, they only managed to get a precision score of 50%, whereas XG boosting produced 92%. These tests indicate that the machine learning method for ransomware detection is effective. This study excels in classification and anomaly detection, which is especially noteworthy given that another paper recently classified ransomware variants except for the "white" designation.

목차

Abstract
Introduction
Methods
Data Description
Experiment Design
XG Boost
Result
Conclusion
References

키워드

저자

• 윤금빛달 [ 한양대학교 공과대학 정보시스템학과 ]
• 전민종 [ 한양대학교 공과대학 정보시스템학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658