• 정보처리학회논문지C
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• 제11C권6호
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• pp.719-724
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• 2004

현재는 인터넷 이용자들이 급격하게 증가하고 있으며, 초보수준의 일반 네트워크 사용자들도 인터넷상의 공개된 해킹 도구들을 사용하여 고도의 기술을 요하는 침임이 가능해졌기 때문에 해킹 문제가 더욱 심각해지고 있다. 해커들이 침입하기 위하여 취약점을 알아내려고 의도하는 다양한 형태의 침입시도들을 탐지하여 침입이 일어나는 것을 사전에 방어할 수 있는 침입시도탐지가 적극적인 예방 차원에서 더욱 필요하다. 기존의 포트 스캔이나 네트워크 취약점 검색 공격에 대응하기 위한 네트워크 기반의 비정상 침입시도 탐지 알고리즘들은 침입시도탐지에 있어 몇 가지 한계점을 갖고 있다. 기존 알고리즘들의 취약성은 Slow Scan과 Coordinated Scan을 한 경우 탐지한 수 없다. 따라서, 침입시도 유형에 제한을 받지 않고 침입시도에 관한 다양한 형태의 비정상 접속을 효과적으로 탐지할 수 있는 새로운 개념의 알고리즘이 요구된다. 본 논문에서는 평상시 정상적인 서비스 패턴을 가지고 그 패턴과 다른 비정상 서비스 패턴이 보이면 이를 침입시도로 탐지하는 개념의 SPAD(Session Pattern Anomaly Detector) 기법을 제안한다.

• 천문학회지
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• 제51권4호
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• pp.111-117
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• 2018

A numerical method is proposed to calculate the response of detectors measuring particle energies from incident isotropic fluxes of electrons and positive ions. The isotropic flux is generated by injecting particles moving radially inward on a hypothetical, spherical surface encompassing the detectors. A geometric projection of the field-of-view from the detectors onto the spherical surface allows for the identification of initial positions and momenta corresponding to the clear field-of-view of the detectors. The contamination of detector responses by particles penetrating through, or scattering off, the structure is also similarly identified by tracing the initial positions and momenta of the detected particles. The relative contribution from the contaminating particles is calculated using GEANT4 to obtain the geometric factor of the instrument as a function of the energy. This calculation clearly shows that the geometric factor is a strong function of incident particle energies. The current investigation provides a simple and decisive method to analyze the instrument geometric factor, which is a complicated function of contributions from the anticipated field-of-view particles, together with penetrating or scattered particles.

• 한국동물위생학회지
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• 제30권3호
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• pp.421-428
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• 2007

This study was conducted two kinds of aims: 1) to modify the analytical methods (conditions) by high performance liquid chromatography - fluorescence detector for the detection of residual ivermectin in raw milk, 2) to provide basic information for the evaluation of standard of the residual ivermectin in raw milk. It could be considerable that negative ion spectra can be better method in the LC/MS analysis for the detection of residues, Characteristic daughter ions were observed in negative ion spectra, however, linear line was not formed in positive ion one. Three Holstein cows ($500{\pm}10kg$) were applied to commercial ointment of ivermectin just one time at the first day of test, and residues in raw milk were examined for 20day after administration. The limit of detection (LOD) was 0.65ng (n=5) by HPLC/FLD, and recovery rates were $87.85%{\sim}99.47%$. The peak was observed at the 4th day, and residues lasted to the end. Thus ivermectin was prohibited when lactating.

• 음성과학
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• 제10권1호
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• pp.95-105
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• 2003

This paper describes a new parameter for voice activity detection which serves as a front-end part for automatic speech recognition systems. The new parameter called run-ratio is derived from the runs test statistic which is used in the statistical test for randomness of a given sequence. The run-ratio parameter has the property that the values of the parameter for the random sequence are about 1. To apply the run-ratio parameter into the voice activity detection method, it is assumed that the samples of an inputted audio signal should be converted to binary sequences of positive and negative values. Then, the silence region in the audio signal can be regarded as random sequences so that their values of the run-ratio would be about 1. The run-ratio for the voiced region has far lower values than 1 and for fricative sounds higher values than 1. Therefore, the parameter can discriminate speech signals from the background sounds by using the newly derived run-ratio parameter. The proposed voice activity detector outperformed the conventional energy-based detector in the sense of error mean and variance, small deviation from true speech boundaries, and low chance of missing real utterances

• Nuclear Engineering and Technology
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• 제49권6호
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• pp.1358-1367
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• 2017

The data integration with modeled predictions (DIMP) model is a promising inverse radiation transport method for solving the special nuclear material (SNM) holdup problem. Unlike previous methods, DIMP is a completely passive nondestructive assay technique that requires no initial assumptions regarding the source distribution or active measurement time. DIMP predicts the most probable source location and distribution through Bayesian inference and quasi-Newtonian optimization of predicted detector responses (using the adjoint transport solution) with measured responses. DIMP performs well with forward hemispherical collimation and unshielded measurements, but several considerations are required when using narrow-view collimated detectors. DIMP converged well to the correct source distribution as the number of synthetic responses increased. DIMP also performed well for the first experimental validation exercise after applying a collimation factor, and sufficiently reducing the source search volume's extent to prevent the optimizer from getting stuck in local minima. DIMP's simple point detector response function (DRF) is being improved to address coplanar false positive/negative responses, and an angular DRF is being considered for integration with the next version of DIMP to account for highly collimated responses. Overall, DIMP shows promise for solving the SNM holdup inverse problem, especially once an improved optimization algorithm is implemented.

• Journal of information and communication convergence engineering
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• 제7권1호
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• pp.7-12
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• 2009

The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.119-124
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• 2003

본 논문에서는 생체의 면역계에서 중요한 역할을 하는 세포독성 T세포의 생성과정의 하나인 긍정 선택(positive selection)과 부정 선택(negative selection)을 모델링하였다. 그리고 침입에 의한 데이터 변경과 바이러스에 의한 데이터 감염 등을 탐지할 때 가장 중요한 요소인 변경 검사 알고리즘을 개발하였다. 제안된 방법은 자기를 인식하는 MHC 인식부와 비자기를 인식하는 항원 인식부를 생성하는 알고리즘이다. 따라서 제안한 방법은 실제 면역세포와 마찬가지로 자신과 침입자 모두에 대한 인식기를 가지고 변경을 탐지하게 된다. 시뮬레이션을 통하여 자기파일의 국소가 변경되었을 때와 블록이 변경되었을 때에 대하여 두 가지 방법을 이용한 변경 검사 알고리즘의 특성과 유효성을 밝힌다

• 한국식품과학회지
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• 제32권4호
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• pp.788-791
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• 2000

키토올리고당의 정량법으로 기존의 비색법과 HPLC방법을 비교 검토하였다. HPLC에 의한 키토올리고당의 정량은 키토올리고당을 D-glucosamine로 분해한 다음, D-glucosamine을 분석하므로서 가능하였다. D-glucosamine를 분석하기 위한 HPLC 분석 조건은 검출기로서 RI detector 그리고 컬럼으로 Bondclone10 $NH_{2}$ column$(330{\times}3.9\;mm,\;10\;micron,\;Phenomenex)$을 사용하고 이동상으로는 acetonitrile : $H_{2}O$(65 : 35)으로 하였다. 키토올리고당의 정량은 비색법 보다 HPLC법이 높은 회수율을 보였으며, 분석시간도 시료당 10분 이내로서 비색법의 4 시간 보다 더 짧았다. 비색법은 키토산 등에 의한 false positive response가 일어났으나 HPLC법은 glucosamine 이외의 다른 성분에 의한 영향을 받지 않았다. 따라서 HPLC에 의한 키토올리고당의 정량은 가능하였으며 기존의 비색법 보다 신속 정확하였다.

• 한국동물위생학회지
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• 제28권3호
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• pp.235-243
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• 2005

This surveies were carried out to investigate the residual levels of fluoroquinolones in chicken and quail eggs by bioassay and HPLC method. The eggs of 240 samples collected from market and farm in Gwangju Metropolitan city were examined from May to December in 2003. Residual antibiotic materials were detected from 47 samples of the 240 eggs by bioassay. Of the 240 eggs assayed, ciprofloxacin, danofloxacin, norfloxacin, ofloxacin, orbifloxacin and perfloxacin were not detected but enrofloxacin was detected from 5 samples in 228 chicken eggs and 1 sample in 12 quail eggs using HPLC with fluorescence detector by multi-residue method. 2 sample eggs in 6 sample which were detected by HPLC were not positive with bioassay. The average residual concentration of enrofloxacin was 0.494 mg/kg in 6 positive samples. The highest residual concentration of enrofloxacin was 1.83mg/kg.

• The Korean Journal of Physiology and Pharmacology
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• 제23권2호
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.