• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.351-353
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• 2020

급변하는 현대사회에서 각종 범죄가 고도로 지능화, 전문화 되고 있을 뿐 만 아니라 매년 범죄율이 증가하고 있다. 범죄수사에서 범죄자를 검거하기 위해서는 '골든 타임'인 초기 단계가 가장 중요하다. 따라서 CCTV를 일일이 돌려보던 기존의 비효율적인 수사방식이 아닌, 얼굴인식기술을 활용해 골든타임 안에 범죄자를 검거 할 수 있도록 도와주는 얼굴인식기반 범죄수사 시스템을 제안한다. 얼굴인식 프로그램을 사용하여 CCTV 영상 속 범죄자가 있다면, 곧바로 얼굴을 인식해 표시 해줌으로써 단시간 안에 범죄자의 이동경로를 파악한다. 이후 이동경로 및 수사정보를 웹페이지를 통해 다른 경찰관들과 공유해 범죄자를 빠르게 검거하는 시스템을 제작하였다. 제작과정에서 얼굴인식관련 기술은 Deep Metric CNN(triplet), Resnet, Knn classification을 사용해 python으로 구현하였다. 통신을 위한 웹서버는 Bitnami를 통해 구축했으며, NAT, DHCP, Port Fowarding 기술을 사용했다. 마지막으로 웹페이지는 HTML, PHP, CSS 등을 통해 제작해 수사정보를 주고 받을 수 있게 제작하였다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.35-42
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• 2014

In Korea, all investigations of maritime crime such as murder, robbery, drug trafficking, and illegal fishing that have been committed at sea are under the jurisdiction of maritime police. As maritime crimes tend to have more unique cases, cause damage on a larger scale, and be limited to evidence that is possibly contaminated, compared to non-maritime crimes, the cases demand maritime police investigators with a higher level of expertise. However, there is currently a lack of competent investigators in the maritime police, leading to the training of maritime police investigators in training agencies. so many of maritime investigators goes to land police training agency including Korean Police Investigation Academy. Therefore, in order to improve the capabilities of maritime crime investigation we must first gather information and data in relation to maritime crimes to foster more investigation specialists, and establish specialized training agencies offering a training course more focused on maritime crimes.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.81-87
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• 2015

This study will show the digital forensic model which fights against cyber-crimes to prepare various cyber-crimes. The digital forensic model will be more useful about the investigation of cyber-crimes and arresting criminals after researching the uses of the digital forensic model and cyber-crime rates in South Korea. This model conduct the standardized data with various languages by the language support system through the digital forensic analyzer. This model will send the data to law enforcement reviewing whether or not we ought to prove criminal charges. Moreover, law enforcement can access the file system to find out admissibility of evidence. And this model simplifies lawful investigation about additional investigation. The data, which is conducted and saved by the digital forensic system, will be helpful to protect against the future crimes because of the data.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.397-402
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• 2006

디지털 범죄 수사 능력은 디지털 포렌식 기술 개발 뿐만 아니라 정책적인 수사체계가 얼마나 잘되어 있느냐에 따라 달라진다. 점차 다양화 되고 지능화 되어가는 디지털 범죄를 수사하기 위해서는 디지털 범죄 수사 체계 모델링이 필요하다. 따라서 본고에서는 디지털 범죄의 종류와 그에 적합한 수사 절차를 언급하고, UML(Unified Modeling Language)을 이용하여 디지털 범죄 수사 절차를 체계화하고 모델링하는 방법을 제시하고자 한다.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.219-220
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• 2018

경찰은 2015년 '범죄피해자 보호 원년'을 선포하고 전국 경찰서에 피해자전담경찰관을 배치하는 등 지속적인 보호조치를 실시하였고, 2018년에는 경찰법과 경찰관직무집행법의 경찰 임무와 경찰관 직무 범위에 '범죄피해자 보호'를 명시해서 범죄피해자 보호를 강화하고 있지만, 강력사건에 피해자전담경찰관이 현장 출동자와 동행하도록하는 등 현재보다 경찰의 범죄피해자 보호제도가 더 확충되어야 하며, 사건의 가해자에 대한 철저한 수사도 필요하다. 경찰은 범죄가 발생하지 않도록 예방하여야 하고, 범죄가 발생한 경우에는 수사 전과정에서 범죄피해자 보호중심으로 수사하여야 국민의 신뢰도 얻고 실체진실발견에도 도움이 될 것이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.622-625
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• 2021

본 논문에서 제안한 주요 범죄사실 개요 분석을 통한 범죄사실 타임라인 자동 작성 시스템은 범죄를 수사하는 현장 수사관들이 수사과정에서 취득한 수사문건 중 범죄사실 파트에 기재된 문장들을 자연어분석을 통해 주요 범죄사실을 파악하는데 필요한 요소들을 자동으로 알아내고, 이 요소들을 타임 라인 상에 시간 순으로 표시함으로써 주요범죄사실 개용을 쉽게 이해 할수 있도록 타임라인을 자동 만들어 내는 시스템에 관한 연구이다. 이를 위해 수사문건에 포함된 범죄사실 개요 범위를 자동으로 알아내고, 그 범위내 포함된 문장들 속에서 주요행위자, 관심을 둬야 하는 시각, 그 시각에 벌어진 주요 행위들을 자동으로 분석하는 연구를 소개한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1687-1692
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• 2009

Cyber forensics is used the process and technology of digital forensics as a criminal investigation in cyber space. Cyber crime is classified into cyber terror and general cyber crime, and those two classes are connected with each other. The investigation of cyber terror requires high technology, system environment and experts, and general cyber crime is connected with general crime by evidence from digital data in cyber space. Accordingly, it is difficult to determine relational crime types, collect evidence and the legal admissibility of evidence. Therefore, we considered the classifications of cyber crime, the collection of evidence in cyber space and the application of laws to cyber crime. In order to efficiently investigate cyber crime, it is necessary to integrate those concepts for each cyber crime-case. In this paper, we constructed a cyber forensics domain ontology for cyber criminal investigation using the concepts, relations and properties, according to categories of cyber crime, laws, evidence, and information of criminals and crime-cases. This ontology can be used in the process of investigating of cyber crime-cases, and for data mining of cyber crime; classification, clustering, association and detection of crime types, crime cases, evidences and criminals.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.161-169
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• 2014

Digital forensics is the process of proving criminal charges by collecting and analyzing digital evidence which is related to the crime in question. Most digital forensic research is focused on digital forensic techniques themselves or cyber crime. In this paper, we designed a digital forensics-criminal investigation linked model in order to effectively apply digital forensics to various types of criminal investigations. Digital forensic ontology was developed based on this model. For more effective application of digital forensics to criminal investigation we derived specific application fields. The ontology has legality rules and adequacy rules, so it can support investigative decision-making. The ontology can be developed into an intelligent criminal investigation system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.767-780
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• 2015

In recent years, as smart phone penetration rate is growing explosively, new forms of cyber crime data is poured out beyond the limits of management system for cyber crime investigation. These new forms of data are collected and stored in police station but, some of data are not systematically managed. As a result, investigators sometimes miss the hidden data which can be critical for a case. Crime data is usually generated by computer which produces complex and huge data and records many logs automatically, so it is necessary to simplify a collected data and cluster by crime pattern. In this paper, we categorize all kinds of cyber crime and simplify crime database and extract critical clues relative to other cases. Through data mining and network-visualization, we found there is correlation between clues of a case. From this result, we conclude cyber crime data mining helps crime prevention, early blocking and increasing the efficiency of the investigation.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.13-20
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• 2022

This study is to develop a named entity recognition model specialized in criminal investigation domains using deep learning techniques. Through this study, we propose a system that can contribute to analysis of crime for prevention and investigation using data analysis techniques in the future by automatically extracting and categorizing crime-related information from text-based data such as criminal judgments and investigation documents. For this study, the criminal investigation domain text was collected and the required entity name was newly defined from the perspective of criminal analysis. In addition, the proposed model applying KoELECTRA, a pre-trained language model that has recently shown high performance in natural language processing, shows performance of micro average(referred to as micro avg) F1-score 98% and macro average(referred to as macro avg) F1-score 95% in 9 main categories of crime domain NER experiment data, and micro avg F1-score 98% and macro avg F1-score 62% in 56 sub categories. The proposed model is analyzed from the perspective of future improvement and utilization.