• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.75-81
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• 2003

User authentication, including confidentiality, integrity over untrusted networks, is an important part of security for systems that allow remote access. Using human-memorable Password for remote user authentication is not easy due to the low entropy of the password, which constrained by the memory of the user. This paper presents a new password authentication and key agreement protocol suitable for authenticating users and exchanging keys over an insecure channel. The new protocol resists the dictionary attack and offers perfect forward secrecy, which means that revealing the password to an attacher does not help him obtain the session keys of past sessions against future compromises. Additionally user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the server. It does not have to resort to a PKI or trusted third party such as a key server or arbitrator So no keys and certificates stored on the users computer. Further desirable properties are to minimize setup time by keeping the number of flows and the computation time. This is very useful in application which secure password authentication is required such as home banking through web, SSL, SET, IPSEC, telnet, ftp, and user mobile situation.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.137-152
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• 2004

Assesment of 30 nutritional minerals and 8 toxic elements in hair are very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in the body. A test has been developed that serves this purpose exceedingly well. This test is known as tissue mineral analysis(TMA). TMA is very popular method in hair mineral analysis for health care professionals in over 46 countries' medical center. However, there are some problems. First, they do not have database which is suitable for korean to do analyze. Second, as the TMA results from TEI-USA is composed of english documents and graphic files prohibited to open, its usability is very low. Third, some of them has low level database which is related to TMA, so hairs are sent to TEI-USA for analyzing and medical services. it bring about an severe outflow of dollars. Finally, TMA results are based on the database of american health and mineral standards, it is possibly mislead korean mineral standards. The purposes of this research is to develope the first Intelligent Medical Expert System(IMES) of TMA, in Korea, which makes clear the problems mentioned earlier IMES can analyze the tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods. Pilot test of this systems are increased of business efficiency and business satisfaction 86% and 92% respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.440-446
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• 2016

Military secrets or confidential data of any organization are extremely important assets. They must be discluded from outside. To do this, methods for detecting anomalous attacks and intrusions inside the network have been proposed. However, most anomaly-detection methods only cover aspects of intrusion from outside and do not deal with internal leakage of data, inflicting greater damage than intrusions and attacks from outside. In addition, applying conventional anomaly-detection methods to data exfiltration creates many problems, because the methods do not consider a number of variables or the internal network environment. In this paper, we describe issues considered in data exfiltration detection for anomaly detection (DEDfAD) to improve the accuracy of the methods, classify the methods as profile-based detection or machine learning-based detection, and analyze their advantages and disadvantages. We also suggest future research challenges through comparative analysis of the issues with classification of the detection methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.67-75
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• 2022

When acquiring a product having an OS, it is very important to identify the exact kernel version of the OS. This is because the product's administrator needs to keep checking whether a new vulnerability is found in the kernel version. Also, if there is an acquisition requirement for exclusion or inclusion of a specific kernel version, the kernel identification becomes critical to the acquisition decision. In the case of the Linux kernel used in various equipment, sometimes it becomes difficult to pinpoint the device's exact version. The reason is that many manufacturers often modify the kernel to produce their own firmware optimized for their device. Furthermore, if a kernel patch is applied to the modified kernel, it will be very different from its base kernel. Therefore, it is hard to identify the Linux kernel accurately by simple methods such as a specific file existence test. In this paper, we propose a static method to classify a specific kernel version by analyzing function names stored in the symbol table. In an experiment with 100 Linux devices, we correctly identified the Linux kernel version with 99% accuracy.