• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.103-113
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• 2003

Program Behavior Intrusion Detection Technique analyses system calls that called by daemon program or root authority, constructs profiles. and detectes anomaly intrusions effectively. Anomaly detections using system calls are detected only anomaly processes. But this has a Problem that doesn't detect affected various Part by anomaly processes. To improve this problem, the relation among system calls of processes is represented by bayesian probability values. Application behavior profiling by Bayesian Network supports anomaly intrusion informations . This paper overcomes the Problems of various intrusion detection models we Propose effective intrusion detection technique using Bayesian Networks. we have profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions we had simulation by proposed normal behavior profiling technique using UNM data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.845-851
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• 2005

웹 서비스는 매우 가깝고 편리한 인터넷 서비스중의 하나이다. 이러한 웹 서비스의 이용이 증가함으로써 웹을 이용한 공격과 취약점 분석으로 위한 위험성이 급격하게 증가하고 있다. 이는 웹 서비스가 가지고 있는 공개성 때문이다. 이에 본 논문에는 웹 공격과정에서 필요한 정보를 얻거나 어플리케이션의 취약점을 찾는 웹 스캔공격을 탐지하기 위한 방법으로 웹 서버의 파일 리스트를 이용하는 방법을 제안하고자 한다. 시스템의 설계와 구현을 위해 사용한 감사 데이터는 Snort에서 일차적으로 탐지된 것을 제외한 웹 서버의 접근로그를 사용한다. 생성된 감사 데이터와 파일 리스트를 비교하여 사용자 요청의 존재여부로 공격을 탐지하도록 설계하였다. 이와 같은 방법은 제안시스템의 실험을 통하여 웹 스캔 공격의 탐지에 효과가 있는 것으로 밝혀졌다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.478-480
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• 2003

공유메모리 기반의 OpenMP 프로그램에서 발생하는 경합은 의도하지 않은 비결정적 수행 결과를 초래하므로 효과적으로 경합을 탐지하는 도구가 필요하다. 본 연구는 OpenMP 프로그램의 경합탐지를 위한 Intel 사의 Thread Checker를 내포병렬성의 여부와 접근사건들의 분포 형태를 기준으로 개발한 커널프로그램 집합을 이용하여 분석한 결과로서, 스레드들을 순서적으로 수행하면서 내포된 스레드를 부모 스레드와 동일한 스레드로 간주하고 적어도 하나의 읽기와 쓰기 접근사건들을 유지하면서 수행중에 경합을 탐지하는 도구임을 보인다. 이 도구는 접근사건의 발생 시에 이전 접근사건들과의 경합 여부를 검사한 후에 그 접근사건의 유지 여부를 결정하므로, 논리적 병행성 관계를 반영하지 못하는 내포된 스레드가 존재하지 않으면 경합의 존재를 검증한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.862-865
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• 2011

지난 3 월 3 일 발생한 분산서비스 거부 공격의 경우 보안 패치 업데이트를 방해하는 현상이 신고되어 공격 시작 전에 악성코드가 분석됨으로 초동 대응이 가능하였다. 하지만 일반적인 분산서비스 거부 공격은 이러한 초동 분석이 불가능한 경우가 대부분이다. 따라서 네트워크에서 공격 트래픽을 효과적으로 탐지 차단하는 DDoS 탐지 엔진이 필요하다. 또한 빠른 트래픽 증가로 인하여 10Gbps Ethernet 사용이 일반화 되고 있고, 이미 수 백 Gbps 의 공격 트래픽이 수시로 발생하고 있다. 본 논문에서는 선로 속도 10Gbps 성능의 분산서비스거부 공격 탐지 칩 셋의 구현에 대해 기술한다. 칩 구현을 위한 고려 사항, 엔진 구조, 하드웨어 합성 결과 및 시스템에 장착된 칩의 성능에 대하여 소개하고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.587-604
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• 2021

Because the library cannot be run independently and used by many applications, it is important to detect vulnerabilities in the library. Fuzzing, which is a dynamic analysis, is used to discover vulnerabilities for the library. Although this fuzzing technique shows excellent results in terms of code coverage and unique crash counts, it is difficult to apply its effects to library fuzzing. In particular, a fuzzing executable and a seed corpus are needed that execute the library code by calling a specific function sequence and passing the input of the fuzzer to reproduce the various states of the library. Generating the fuzzing environment such as fuzzing executable and a seed corpus is challenging because it requires both understanding about the library and fuzzing knowledge. We propose a novel method to improve the ease of library fuzzing and enhance code coverage and crash detection performance by using a test framework. The systems's performance in this paper was applied to nine open-source libraries and was verified through comparison with previous studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.361-370
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• 2015

Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.507-521
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• 2021

Detecting outliers among high-dimensional data encounters a challenging problem of screening the variables since relevant information is often contained in only a few of the variables. Otherwise, when a number of irrelevant variables are included in the data, the distances between all observations tend to become similar which leads to making the degree of outlierness of all observations alike. The subspace outlier detection method overcomes the problem by measuring the degree of outlierness of the observation based on the relevant subsets of the entire variables. In this paper, we survey recent subspace outlier detection techniques, classifying them into three major types according to the subspace selection method. And we summarize the techniques of each type based on how to select the relevant subspaces and how to measure the degree of outlierness. In addition, we introduce some computing tools for implementing the subspace outlier detection techniques and present results from the simulation study and real data analysis.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1229-1241
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• 2014

Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.