• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.35-42
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• 2010

This paper reviews the current studies about the current secure OS, security module and SELinux, and suggests Linux access control module that uses the user discriminating authentication, security authority inheritance of subjects and objects, reference monitor and MAC class process and real-time audit trailing using DB. First, during the user authentication process, it distinguishes the access permission IP and separates the superuser(root)'s authority from that of the security manager by making the users input the security level and the protection category. Second, when the subjects have access to the objects through security authority inheritance of subjects and objects, the suggested system carries out the access control by comparing the security information of the subjects with that of the objects. Third, this system implements a Reference Monitor audit on every current events happening in the kernel. As it decides the access permission after checking the current MAC security attributes, it can block any malicious intrusion in advance. Fourth, through the real-time audit trailing system, it detects all activities in the operating system, records them in the database and offers the security manager with the related security audit data in real-time.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.15-23
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• 2011

In this paper, we propose a keyboard security method that is based on a subclassing. This method doesn't need an additional hardware and can be applied to Web browsers that do not support ActiveX controls. As the users of Web browsers such as Firefox, Safari, Chrome etc. are increased, it is more required to have the keyboard security methods that are based on software and don't use ActiveX controls. Thus we developed the user mode keyboard security method that is based on a subclassing with plugins. Our method doesn't need an additional hardware module and is interoperable with general kernel mode security programs.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.703-707
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• 2008

This paper designs and implements a software prober to support a more effective power analysis for Linux-based embedded systems by using power consumption data measured in hardware. The proposed software prober, which is included in a target system as a module, collects power data and provides a service to discover major power consumers in user functions of applications or kernel functions through time synchronization between the power data measured in hardware and software. In addition, we verified usefulness of the implemented software prober by running user applications on target systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.971-974
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• 2002

일반적으로 악의적인 사용자는 시스템에 공격을 가해 관리자 권한을 취득한 후 그 시스템에 쉽게 침입하기 위해 백도어를 설치해 놓는다. 이전의 백도어는 대부분 사용자 영역에서 수행중인 응용 프로그램의 형태로 설치가 되었다. 그러나 최근에는 로더블 모듈과 같은 운영체제의 확장 방법을 이용하여 커널 영역에서 수행되는 백도어가 나타나게 되었다. 이러한 커널 백도어를 구현하는 방식은 크게 시스템 콜 테이블을 수정하는 방법과 시스템 콜 처리 루틴을 수정하는 방법의 두 가지로 나눌 수 있다. 본 논문에서는 기존에 구현된 커널 백도어의 특성 분석을 하였으며, 이를 기반으로 커널 백도어를 효율적으로 차단 및 탐지할 수 있는 방안을 제안하고 있다. 본 논문에서 언급하는 방안은 커널 메모리 영역에 대한 분석을 통하여 백도어가 시스템 콜 테이블을 수정하거나 시스템 콜 처리 루틴을 변경할 수 없도록 하는 보호 메커니즘을 적용하고 있다. 이를 통하여 커널 내부로 적재되어 백도어를 생성하는 악의적인 모듈의 가능성을 원천적으로 방지할 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.949-960
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• 2019

Due to miniaturization of electronic devices and development of IoT(Internet of Things), embedded system have been used in various field. Meanwhile, there is a potential vulnerability by the insufficient of system's security. In this paper, we implement secure boot using TPM to protect the integrity of embedded system environment. The Suggestion considers the required availability in the embedded system and detects the system's tampering at secure boot process via TPM. In addition, we have reinforced the confidentiality through AES encryption of the kernel at secure boot.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.151-158
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• 2019

With the recent advances of memory technologies, high-performance non-volatile memories such as non-volatile dual in-line memory module (NVDIMM) have begun to be used as an addition or an alternative to server-side storages. When these memory bus-connected storages (MBSs) are installed over non-uniform memory access (NUMA) servers, the distance between NUMA nodes and MBSs is one of the crucial factors that influence file processing performance, because the access latency of a NUMA system varies depending on its distance from the NUMA nodes. This paper presents the design and implementation of a high-performance logical volume manager for MBSs, called MBS-LVM, when multiple MBSs are scattered over a NUMA server. The MBS-LVM consolidates the address space of each MBS into a single global address space and dynamically utilizes storage spaces such that each thread can access an MBS with the lowest latency possible. We implemented the MBS-LVM in the Linux kernel and evaluated its performance by porting it over the tmpfs, a memory-based file system widely used in Linux. The results of the benchmarking show that the write performance of the tmpfs using MBS-LVM has been improved by up to twenty times against the original tmpfs over a NUMA server with four nodes.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.821-830
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• 2006

As internet worms are spread out worldwide, the detection and filtering of worms becomes one of hot issues in the internet security. As one of implementation methods to detect worms, the Linux Netfilter kernel module can be used. Its basic operation for worm detection is a string matching where coming packet(s) on the network is/are compared with predefined worm signatures(patterns). A worm can appear in a packet or in two (or more) succeeding packets where some part of worm is in the first packet and its remaining part is in its succeeding packet(s). Assuming that the maximum length of a worm pattern is less than 1024 bytes, we need to perform a string matching up to two succeeding packets of 2048 bytes. To do so, Linux Netfilter keeps the previous packet in buffer and performs matching with a combined 2048 byte string of the buffered packet and current packet. As the number of concurrent connections to be handled in the worm detection system increases, the total size of buffer (memory) increases and string matching speed becomes low In this paper, to reduce the memory buffer size and get higher speed of string matching, we propose a string matching scheme without using buffer. The proposed scheme keeps the partial matching result of the previous packet with signatures and has no buffering for previous packet. The partial matching information is used to detect a worm in the two succeeding packets. We implemented the proposed scheme by modifying the Linux Netfilter. Then we compared the modified Linux Netfilter module with the original Linux Netfilter module. Experimental results show that the proposed scheme has 25% lower memory usage and 54% higher speed compared to the original scheme.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.8-17
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• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.