• The KIPS Transactions:PartC
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• v.11C no.6 s.95
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• pp.719-724
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• 2004

Recently, since the number of internet users is increasing rapidly and, by using the public hacking tools, general network users can intrude computer systems easily, the hacking problem is getting more serious. In order to prevent the intrusion, it is needed to detect the sign in advance of intrusion in a positive prevention by detecting the various foms of hackers' intrusion trials to know the vulnerability of systems. The existing network-based anomaly detection algorithms that cope with port- scanning and the network vulnerability scans have some weakness in intrusion detection. they can not detect slow scans and coordinated scans. therefore, the new concept of algorithm is needed to detect effectively the various forms of abnormal accesses for intrusion regardless of the intrusion methods. In this paper, SPAD(Session Pattern Anomaly Detector) is presented, which detects the abnormal service patterns by comparing them with the ordinary normal service patterns.

• Journal of The Korean Astronomical Society
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• v.51 no.4
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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.

• Korean Journal of Veterinary Service
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• v.30 no.3
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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.

• Speech Sciences
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• v.10 no.1
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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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• v.49 no.6
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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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• v.7 no.1
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.119-124
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• 2003

In this paper, we modeled positive selection and negative selection that is developing process of cytotoxic T-cell that plays important role in biological immune system. Also, we developed change detection algorithm, which is very Important part in detecting data change by intrusion and data infection by computer virus. Proposed method is the algorithm that produces MHC receptor lot recognizing self and antigen detector for recognizing non-self. Therefore, proposed method detects self and intruder by two type of detectors like real immune system. We show the effectiveness and characteristics of proposed change detection algorithm by simulation about point and block change of self file.

• Korean Journal of Food Science and Technology
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• v.32 no.4
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• pp.788-791
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• 2000

The quantitative analysis of chitooligosaccharide was compared to using colorimetry and HPLC method. HPLC method required less than 10mins per sample in analytical time of glucosamine and its the recovery rate was 98.4% (10 mg/ml, w/v). Also there was no the effects of interfering substances(false positive response) by HPLC method. The content of chitooligosaccharide in processed chitooligosaccharide products obtained using HPLC showed lower levels compared to colorimetry. Thus, HPLC method was more sensitive, effective and precise than the colorimetry currently used to determine the glucosamine of chitooligosaccharide.

• Korean Journal of Veterinary Service
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• v.28 no.3
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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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• v.23 no.2
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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.