• Title/Summary/Keyword: 범죄스크립트분석

Search Result 3, Processing Time 0.019 seconds

A Study on the Tooling of Money Laundering Using Cryptocurrency (가상화폐를 이용한 자금세탁 도구화에 관한 연구)

  • Song, Hye Jin
    • Journal of the Society of Disaster Information
    • /
    • v.17 no.3
    • /
    • pp.600-607
    • /
    • 2021
  • Purpose: The purpose of this study is to examine the path of money laundering of criminal proceeds through cryptocurrency using criminal script analysis and to devise measures to prevent and prevent criminal justice agencies from doing so. Method: Based on the results of a prior study on the profit path of cryptocurrency through money laundering and criminal cases in Korea, the path of money laundering was analyzed using criminal script techniques. Result: Most of the cryptocurrencies that have been launched are converted into criminal proceeds, which are re-launched and cashed or have a vicious cycle of being used as criminal funds are used. According to the script, the route of money laundering is mainly converted to criminal proceeds from cryptocurrency exchanges using anonymity, which is repeated several times, making it very difficult to find the money using cryptocurrency in criminal justice institutions. Conclusion: As the method of money laundering using cryptocurrency is becoming more sophisticated, legal sanctions and preventive institutionalization should be prepared for the prohibition or confiscation of cryptocurrency transactions for money laundering after understanding the flow.

A Study on the Modus Operandi of Smishing Crime for Public Safety (국민안전을 위한 스미싱 범죄수법분석)

  • Choi, Kwan;Kim, Minchi
    • Convergence Security Journal
    • /
    • v.16 no.3_2
    • /
    • pp.3-12
    • /
    • 2016
  • The purpose of this study is to analyse Modus Operandi of smishing. For the study, 87 cases of smishing crime reports and smishing experiences of victims were analysed and 10 police officers who investigates smishing crime were interviewed. The results indicated that smishing crime can be divided into the preparation stage and the implementation stage. In the preparation stage, two modus operandi patterns, collection of personal information and text message script composition, were identified. In the implementation stage, seven modus operandi patterns were identified: sending smishing text messages and installation of malicious mobile applications, leak personal information, sending personal information to smishing crime organization through online server, payment attempt using collected personal information, intercept authorization code, completion of payment using intercepted authorization code, and payment amount was delivered to victims. Further implications were discussed.

AI Crime Prediction Modeling Based on Judgment and the 8 Principles (판결문과 8하원칙에 기반한 인공지능 범죄 예측 모델링)

  • Hye-sung Jung;Eun-bi Cho;Jeong-hyeon Chang
    • Journal of Internet Computing and Services
    • /
    • v.24 no.6
    • /
    • pp.99-105
    • /
    • 2023
  • In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.