• Convergence Security Journal
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• v.12 no.5
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• pp.23-32
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• 2012

The police is responsible for protecting nation's property and life. Protecting nation from crime among the core duties is the most important activity of the police. But the big problem on reported crime system of the police was founded in the recent Suwon incident. Unfortunately, the unprofessional response made a toll of human sacrifice. Taking this opportunity, we need to consider closely the problems of the reported crime and system of the police and the solutions on the problems. This study analyzes the reported crime system of the police from the law and institutional and try to seek the solutions. This study searches the management status of the police system and arranges the problems in legal and institutional terms. And then, it arranges the solutions on the problems.

• Journal of the Korean Professional Engineers Association
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• v.32 no.4
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• pp.49-53
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• 1999

• Korean Security Journal
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• no.62
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• pp.185-203
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• 2020

While overall crime has been on the decline since 2009, voice phishing has rather been on the rise. The government and academia have presented various measures and conducted research to eradicate it, but it is not enough to catch up with evolving voice phishing. In the study, researchers focused on catching criminals and preventing damage from voice phishing, which is difficult to recover from. In particular, a voice phishing prediction method using the Fraud Detection System (FDS), which is being used to detect financial fraud, was studied based on the fact that the victim engaged in financial transaction activities (such as account transfers). As a result, it was conceptually derived to combine big data such as call details, messenger details, abnormal accounts, voice phishing type and 112 report related to voice phishing in machine learning-based Fraud Detection System(FDS). In this study, the research focused mainly on government measures and literature research on the use of big data. However, limitations in data collection and security concerns in FDS have not provided a specific model. However, it is meaningful that the concept of voice phishing responses that converge FDS with the types of data needed for machine learning was presented for the first time in the absence of prior research. Based on this research, it is hoped that 'Voice Phishing Damage Prediction System' will be developed to prevent damage from voice phishing.