• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.163-170
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• 2014

Cyberspace is closely related with one another, transcending the spacetime throughout the world, and is already located in the most important area of our lives. However, if the organizational cyber terror happens like the national paralysis crisis of major information network such as the previous 1.25 the Internet crisis, the possibility of cyber crisis highly damaging our whole society such as the leakage of the national secrecy and advanced technology is increasing. But we haven't set up the institutional procedure systematically performing the national cyber security management affairs yet. So, in case of cyber crisis, this is highly likely to damage the aspects of national and personal level. On this point, this study looks into the examples of legislation related to our cyber security, and suggests the implication on the revision direction of national cyber security management regulations through relative examination about the examples of legislation in major countries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.1018-1023
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• 2019

Since 2010, vehicle black boxes have become popular with many people, if there is no record of the vehicle accident scene, or if the offender deliberately deletes the image data, the victim succeeds. The biggest advantage of blockchain is that it is impossible to modify and delete data by data distribution storage. The biggest disadvantage is that sensitive data is also distributed. In this paper, we propose a blockchain method for the black box by using the advantage of shared block data and we intend to solve the problem of personal information leakage which is a disadvantage of blockchain by storing sensitive information stored in a blockchain in a private server by LINK blockchain with a private server. We also attached code(Github) and demonstration video(Youtube) linking LINK blockchain with the private server in this paper.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.647-656
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• 2020

Recently, there has been an increasing number of cases in which important data (personal information, technology, etc.) of national and public institutions are leaked to the outside world. Surveys show that the largest cause of such leakage accidents is "insiders." Insiders of organization with the most authority can cause more damage than technology leaks caused by external attacks due to the organization. This is due to the characteristics of insiders who have relatively easy access to the organization's major assets. This study aims to present an optimized property selection model for detecting such abnormalities through supervised learning algorithms among machine learning techniques using actual data such as CrossNet data transfer system transmission log, e-mail transmission log, and personnel information, which safely transmits data between separate areas (security area and non-security area) of the business network and the Internet network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1311-1319
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• 2019

Currently, the web environment is a commonly used area for sharing information and conducting business. It is becoming an attack point for external hacking targeting on personal information leakage or system failure. Conventional signature-based detection is used in cyber threat but signature-based detection has a limitation that it is difficult to detect the pattern when it is changed like polymorphism. In particular, injection attack is known to the most critical security risks based on web vulnerabilities and various variants are possible at any time. In this paper, we propose a novelty detection technique to detect abnormal state that deviates from the normal state on web-server log dataset(WSLD). The proposed method is a machine learning-based technique to detect a minor anomalous data that tends to be different from a large number of normal data after replacing strings in web-server log dataset with vectors using machine learning-based embedding algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.7-13
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• 2020

IoT which is the core IT of the 4^th industrial revolution, is providing various services from users in the conversion with other industries. The IoT convergence technology is leading the communication paradigm of communication environment in accordance with the increase of convenience for users. However, it is urgently needed to establish the security measures for the rapidly-developing IoT convergence technology. As IoT is closely related to digital ethics and personal information protection, other industries should establish the measures for coping with threatening elements in accordance with the introduction of IoT. In case when security incidents occur, there could be diverse problems such as information leakage, damage to image, monetary loss, and casualty. Thus, this paper suggests a certificate management technique for safe control over access in IoT-based Cloud convergence environment. This thesis designed the device/user registration, message communication protocol, and device renewal/management technique. On top of performing the analysis on safety in accordance with attack technique and vulnerability, in the results of conducting the evaluation of efficiency compared to the existing PKI-based certificate management technique, it showed about 32% decreased value.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.201-210
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• 2023

Deep learning-based face verificattion model show high performance and are used in many fields, but there is a possibility the user's face image may be leaked in the process of inputting the face image to the model. Althoughde-identification technology exists as a method for minimizing the exposure of face features, there is a problemin that verification performance decreases when the existing technology is applied. In this paper, after combining the face features of other person, a de-identified face image is created through StyleGAN. In addition, we propose a method of optimizingthe combining ratio of features according to the face verification model using HopSkipJumpAttack. We visualize the images generated by the proposed method to check the de-identification performance, and evaluate the ability to maintain the performance of the face verification model through experiments. That is, face verification can be performed using the de-identified image generated through the proposed method, and leakage of face personal information can be prevented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.755-758
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• 2016

The use of smart phones has had a great impact on the mobile internet business. It shows a lot of growth in the healthcare sector not only commerce, advertising, billing, games, video content, media, amd O2O business. The United States has eased the regulations for healthcare apps smart phone devices in 2015, and China has established a five-year road map to solve shortage of doctors and hospital beds by utilizing mobile devices such as wearable in the same year. The application of wearable devices in the medical field is gradually increasing in Korea too, but there is a security problem as leading challenge. Security incidents in non-ICT sectors such as financial, medical, etc. have increased by using ICT each year. Personal information leakage is also increasing in field likely occurring the potential secondary damages such as financial fraud, illegal promotions, insurance and pharmaceutical companies abuse. In this study, we analyze malwares as the mobile threats, the five risks of mobile smart phone, mobile use cases and the mobile threat countermeasures for healthcare.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.97-110
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• 2022

Federated learning is a learning method capable of performing model learning without transmitting learning data. The IoT or healthcare field is sensitive to information leakage as it deals with users' personal information, so a lot of attention should be paid to system design, but when using federated-learning, data does not move from devices where data is collected. Accordingly, many federated-learning implementations have been developed, but detailed research on system design for the development and operation of systems using federated learning is insufficient. This study shows that measures for the life cycle, code version management, model serving, and device monitoring of federated learning are needed to be applied to actual projects and distributed to IoT devices, and we propose a design for a development environment that complements these points. The system proposed in this paper considered uninterrupted model-serving and includes source code and model version management, device state monitoring, and server-client learning schedule management.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.269-275
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• 2022

As artificial intelligence services targeting humans increase, social demands are increasing that artificial intelligence should also be made on an ethical basis. Following this trend, the government and businesses are preparing policies and norms related to artificial intelligence ethics. In order to establish reasonable policies and norms, the first step is to understand the public's perceptions. In this paper, social data and news comments were collected and analyzed to understand the public's perception related to artificial intelligence and ethics. Interest analysis, emotional analysis, and discourse analysis were performed and visualized on the collected datasets. As a result of the analysis, interest in "artificial intelligence ethics" and "artificial intelligence" favorability showed an inversely proportional correlation. As a result of discourse analysis, the biggest issue was "personal information leakage," and it also showed a discourse on contamination and deflection of learning data and whether computer-made artificial intelligence should be given a legal personality. This study can be used as data to grasp the public's perception when preparing artificial intelligence ethical norms and policies.