• Korean Security Journal
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• no.54
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• pp.101-125
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• 2018

In the police, crime intelligence is the basis of decision making for police's original activities in response to crime. Police decision making is done in various ways such as investigation and prevention of individual cases, allocation of resources, organization prioritization, etc. The purpose of this study was to investigate the activities of the UK policing in analyzing crime intelligence and to reflect them in the policing and to draw implications for the comparison with the Korean police. The UK operates a central police agency based on the local police system, and establishes a National Intelligence Model (NIM) system that operates crime intelligence throughout the country. In order to respond to crimes and risks through coordination and cooperation, rather than by centralized police activities, the intelligence department of the police agencies should not only prevent and suppress crime through the analysis of integrated crime information, but also make police decision-making. In contrast, the Korea police operate crime intelligence, such as statistics, case intelligence, and there is no integrated way to use it. In addition, there are few cases in which the organizational decision - making based on crime intelligence is utilized efficiently and systematically. For development, it is necessary to construct an integrated management system and analysis organization for crime intellgence. Criminal Intelligence Analysis Organizations should seek to reorganize the role of the current intelligence department or to operate a separate analysis system through the information system while maintaining the current role of each department.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.21-25
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• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Korean Security Journal
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• no.36
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• pp.227-253
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• 2013

The threat of crime became a global issue nowadays. Terrorism, organized crime, crime by nation can be mentioned as typical examples. The crimes in modern society can't be identified to happen when, where and how being different from those traditional crimes(murder, robbery, sexual abuse, arson). This was the result of changed security environment that needs to address wide range of crimes as being indicated sporadic characteristics of modern threat of crime such as terrorism threat targeting unidentified masses as well as the emergence of systemic phenomenon of organized crimes and crime committed by nation. In this regard, the case of 9.11 occurred in 2001 can be deemed as an example that made a dramatic turn around to the security environment. After the terrorism, it provided an opportunity to rethink not only USA but also to the institutions all over the world that deals with crime about gathering, management, utilization of crime intelligence. As a result of which there appeared a change in police activities more effectively in gathering & managing crime information and ILP is the very activity that emerged from the USA/UK countries. This aims police activities to minimize the threat of crime being the system reflecting a framework to manage more directly to control crime by gathering and processing information. In view of the global change of security environment as a common phenomenon, the need to direct to ILP has increased in Korea in line with such security environmental change. Accordingly, this study focused on the method of introduction of ILP and presentation of matters for discussion by reviewing ILP activities of the USA/UK countries.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.301-321
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• 2024

Generative artificial intelligence is currently developing rapidly and expanding industrially. The development of generative AI is expected to improve productivity in most industries. However, there is a probability for exploitation of generative AI, and cases that actually lead to crime are emerging. Compared to the fast-growing AI, there is no legislation to regulate the generative AI. In the case of Korea, the crimes and risks related to generative AI has not been clearly classified for legislation. In addition, research on the responsibility for illegal data learned by generative AI or the illegality of the generated data is insufficient in existing research. Therefore, this study attempted to classify crimes related to generative AI for domestic legislation into generative AI for target crimes, generative AI for tool crimes, and other crimes based on ECRM. Furthermore, it suggests technical countermeasures against crime and risk and measures to improve the legal system. This study is significant in that it provides realistic methods by presenting technical countermeasures based on the development stage of AI.

• Architectural research
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• v.24 no.1
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• pp.1-11
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• 2022

As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.

• Convergence Security Journal
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• v.19 no.2
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• pp.113-121
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• 2019

With the development of the Internet and Information Communication Technology, there has been an increase in the amount of Open Source Intelligence(OSINT). OSINT can be highly effective, if well refined and utilized. Recently, it has been assumed that almost 95% of all information comes from public sources and the utilization of open sources has sharply increased. The ISVG and START programs, for example, collect information about open sources related to terrorism or crime, effectively used to detect terrorists and prevent crime. The open source information related to the cyber attacks is, however, quite different from that in terrorism (or crime) in that it is difficult to clearly identify the attacker, the purpose of attack, and the range of damage. In addition, the data itself of cyber attacks is relatively unstructured. So, a totally new approach is required to establish and utilize an OSINT database for cyber attacks, which is proposed in this paper.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.75-80
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• 2020

Since the Fourth Industrial Revolution, important technologies such as big data analysis, robotics, Internet of Things, and the artificial intelligence have been used in various fields. Generally speaking it is understood that the big-data technology consists of gathering stage for enormous data, analyzing and processing stage and distributing stage. Until now crime records which is one of useful big-sized data are utilized to obtain investigation information after occurring crimes. If crime records are utilized to predict crimes it is believed that crime occurring frequency can be lowered by processing big-sized crime records in big-data framework. In this research the design is proposed that the smart system can provide the users of smart devices crime occurrence probability by processing crime records in big-data analysis. Specifically it is meant that the proposed system will guide safer routes by displaying crime occurrence probabilities on the digital map in a smart device. In the experiment result for a smart application dealing with small local area it is showed that its usefulness is quite good in crime prevention.

• Convergence Security Journal
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• v.15 no.5
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• pp.71-76
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• 2015

Financial Intelligence Unit Law doesn't include investigation on important cases that could influence the security and existence of the nation that are the core jobs of national intelligence agency. So the agency has a difficulty to investigate the international crime of North Korea and other security incidents. It is also difficult to catch an international crime organization working in Korea. It also produces problems such as difficulty in investigating the illegal leak of strategic materials and investigating people related to illegal funding to international terrorism. So it is urgently needed to revise Financial Intelligence Law as soon as possible. Foreign intelligence agencies use the information of financial intelligence unit in many different ways. National Security Agency of China and Australian Security Intelligence Organization freely use the information of financial intelligence unit based on their own laws and systems. Central Intelligence Agency and Federal Bureau of Investigation of USA and Secret Intelligence Service and Security Service of Britain request financial intelligence units to supply them with the information of financial intelligence unit. But the national intelligence agency of Korea isn't able to approach to FIU and can't share the FIU information with foreign intelligence agencies. To solve the problem, they should revise Financial Intelligence Unit Law so that national intelligence agency can receive or request information from Korean Financial Intelligence Unit.