• 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.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.7-13
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• 2021

Recently, due to the many features and advantages of cloud computing, cloud service is being introduced to countless industries around the world at an unbelievably rapid pace. With the rapid increase in the introduction of multi-cloud based services, security vulnerabilities are increasing, and the risk of data leakage from cloud computing services are also expected to increase. Therefore, this study will propose an AWS Well-Architected based security architecture configuration method such as AWS standard security architecture, AWS shared security architecture model that can be applied for personal information security including cost effective of cloud services for better security in AWS cloud service. The AWS security architecture proposed in this study are expected to help many businesses and institutions that are hoping to establish a safe and reliable AWS cloud system.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.79-90
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• 2014

User authentication based on ID and PW has been widely used. As the Internet has become a growing part of people' lives, input times of ID/PW have been increased for a variety of services. People have already learned enough to perform the authentication procedure and have entered ID/PW while ones are unconscious. This is referred to as the adaptive unconscious, a set of mental processes incoming information and producing judgements and behaviors without our conscious awareness and within a second. Most people have joined up for various websites with a small number of IDs/PWs, because they relied on their memory for managing IDs/PWs. Human memory decays with the passing of time and knowledges in human memory tend to interfere with each other. For that reason, there is the potential for people to enter an invalid ID/PW. Therefore, these characteristics above mentioned regarding of user authentication with ID/PW can lead to human vulnerabilities: people use a few PWs for various websites, manage IDs/PWs depending on their memory, and enter ID/PW unconsciously. Based on the vulnerability of human factors, a variety of information leakage attacks such as phishing and pharming attacks have been increasing exponentially. In the past, information leakage attacks exploited vulnerabilities of hardware, operating system, software and so on. However, most of current attacks tend to exploit the vulnerabilities of the human factors. These attacks based on the vulnerability of the human factor are called social-engineering attacks. Recently, malicious social-engineering technique such as phishing and pharming attacks is one of the biggest security problems. Phishing is an attack of attempting to obtain valuable information such as ID/PW and pharming is an attack intended to steal personal data by redirecting a website's traffic to a fraudulent copy of a legitimate website. Screens of fraudulent copies used for both phishing and pharming attacks are almost identical to those of legitimate websites, and even the pharming can include the deceptive URL address. Therefore, without the supports of prevention and detection techniques such as vaccines and reputation system, it is difficult for users to determine intuitively whether the site is the phishing and pharming sites or legitimate site. The previous researches in terms of phishing and pharming attacks have mainly studied on technical solutions. In this paper, we focus on human behaviour when users are confronted by phishing and pharming attacks without knowing them. We conducted an attack experiment in order to find out how many IDs/PWs are leaked from pharming and phishing attack. We firstly configured the experimental settings in the same condition of phishing and pharming attacks and build a phishing site for the experiment. We then recruited 64 voluntary participants and asked them to log in our experimental site. For each participant, we conducted a questionnaire survey with regard to the experiment. Through the attack experiment and survey, we observed whether their password are leaked out when logging in the experimental phishing site, and how many different passwords are leaked among the total number of passwords of each participant. Consequently, we found out that most participants unconsciously logged in the site and the ID/PW management dependent on human memory caused the leakage of multiple passwords. The user should actively utilize repudiation systems and the service provider with online site should support prevention techniques that the user can intuitively determined whether the site is phishing.

• Korean Security Journal
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• no.25
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• pp.109-130
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• 2010

As many countries in the world including the Republic of Korea have used all their national resources in the accelerating economic information warfare, illegal leakage of industrial technologies and information has increased rapidly. The costs required for damage prevention from 2007 to 2008 are estimated at approximately KRW 180 trillion which is expected to increase gradually in future. Because the tricks of leaking key technologies are also getting increasingly systematized, sophisticated and bigger, e.g., simple theft at the individual level or the conspiracy of all the staff taking part in the research activities, we should pay special attention to technology security in addition to technology development. While there are several factors affecting such the brain drain, they usually include personal, social, political and cultural factors, for instance, very heavy educational expenditure of children compared to relatively low pay, the speedy labor market circulation for experienced personnel, or political restrictions on researches. In this context, as part of efforts made to prevent the outflow of core personnel, individual companies and research institutes should establish systematically appropriate core personnel management systems for their own organizational or business goals and principles which are intented to ensure to give better treatment and benefit to core personnel and to exercise closer supervision over them. Furthermore, the conventional personnel management system should be radically and flexibly improved in the manner of encouraging the core personnel returning to the organization to combine their external experiences with practices, instead of penalizing them. At the same time, it is necessary to train and educate core personnel through mutual collaboration and in-house training facilities as well as external academic programs operated jointly at the level of the industry. Finally, as the issues concerning the outflow of core personnel are not just problems of relevant companies and other advanced countries have devoted their best efforts to secure their own key technologies at the national level, it is urgent for the industry and the competent authorities to cooperate closely.