• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.3
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• pp.282-292
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• 2007

The present paper describes the results of high speed photography, acoustic emission (AE) detection and plasma light emission (LE) measurement during $CO_2$ laser welding of 304 stainless steel in different processing conditions. Video images with high spatial and temporal resolution allowed to observe the melt dynamics and keyhole evolution. The existence of keyhole was confirmed by the slag motion on the weld pool. The characteristic frequencies of flow instability and keyhole fluctuations at different welding speed were measured and compared with the results of Fourier analyses of temporal AE and LE spectra. The experimental results were compared with the newly developed numerical model of keyhole dynamics. The model is based on the assumption that the propagation of front part of keyhole into material is due to the melt ejection driven by laser induced surface evaporation. The calculations predict that a high speed melt flow is induced at the front part of keyhole when the sample travel speed exceeds several 10 mm/s. The numerical analysis also shows the hump formation on the front keyhole wall surface. Experimentally observed melt behavior and transformation of the AE and LE spectra with variation of welding speed are qualitatively in good agreement with the model predictions.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.431-438
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• 1999

The environmental control based on interactive thermoregulatory behavior for swine production has many advantages over the conventional temperature-based control methods. Therefore, this study was conducted to compare various feature selection methods using postural images of growing pigs under various environmental conditions. A color CCD camera was used to capture the behavioral images which were then modified to binary images. The binary images were processed by thresholding, edge detection, and thinning techniques to separate the pigs from their background. Following feature were used for the input patterns to the neural network ; \circled1 perimeter, \circled2 area, \circled3 Fourier coefficients (5$\times$5), \circled4 combination of (\circled1 ＋ \circled2), \circled5 combination of (\circled1 ＋ \circled3), \circled6 combination of (\circled2 ＋ \circled3), and \circled7 combination of (\circled1 ＋ \circled2 ＋ \circled3). Using the above each input pattern, the neural network could classify training images with the success rates of 96%, 96%, 96%, 100%, 100%, 96%, 100%, and testing images with those of 88%, 86%, 93%, 96%, 91%, 90%, 98%, respectively. Thus, the combination of perimeter, area and Fourier coefficients of the thinning images as neural network features gave the best performance (98%) in the behavioral classification.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.308-317
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• 2008

The objects real time rendered in the 3D cyber space can interact with each others according to the events which are happened when satisfying some conditions. But to representing the behaviors with these interactions, too many event conditions are considered because each behavior pattern and event must be corresponded in a one-to-one ratio. It leads to problems which increase the system complexity. So, in this paper, we try to physical method based on elasticity force for representing more realistic behaviors of AI fish and apply to the deformable multi-detection sensor, so we suggest the new method which can create the various behavior patterns responding to one evasion event.

• Fisheries and Aquatic Sciences
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• v.14 no.3
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• pp.218-225
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• 2011

Dual frequency identification sonar (DIDSON) is an imaging sonar that has been used for numerous fisheries investigations in a diverse range of freshwater and marine environments. The main purpose of DIDSON is fish counting, fish sizing, and fish behavioral studies. DIDSON records video-quality data, so processing power for handling the vast amount of data with high speed is a priority. Therefore, a semiautomated analysis of DIDSON data for fish counting, sizing, and fish behavior in Echoview (fisheries acoustic data analysis software) was accomplished using testing data collected on the Rakaia River, New Zealand. Using this data, the methods and algorithms for background noise subtraction, image smoothing, target (fish) detection, and conversion to single targets were precisely illustrated. Verification by visualization identified the resulting targets. As a result, not only fish counts but also fish sizing information such as length, thickness, perimeter, compactness, and orientation were obtained. The alpha-beta fish tracking algorithm was employed to extract the speed, change in depth, and the distributed depth relating to fish behavior. Tail-beat pattern was depicted using the maximum intensity of all beams. This methodology can be used as a template and applied to data from BlueView two-dimensional imaging sonar.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.725-736
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• 2010

Nowadays, followed the internet service contents increasing makes also increase attack case on the web system. Usually web attack use mixed many kinds of attack mechanism for successfully attack to the server system. These increasing of the kinds attack mechanism, however web attack defence mechanism is not follow the spread of the attack. Therefore, for the defends web application, web attack should be categorizing and analysing for the effective defense. In this paper, we analyze web attack specification evidence and behavior system that use for effective expressions what we proposed. Also, we generate web attack scenario, it is for using verification of our proposed expressions.

• Macromolecular Research
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• v.11 no.4
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• pp.267-272
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• 2003

The isothermal cure reactions of blends of epoxy (DGEBA, diglycidyl ether of bisphenol A)/anhydride resin with polyamide copolymer (poly(dimmer acid-co-alkyl polyamine)) or PEI were studied using differential scanning calorimetry (DSC). Rheological measurements have been made to investigate the viscosity and mechanical relaxation behavior of the blends. The reaction rate and the final cure conversion were decreased with increasing the amount of thermoplastics in the blends. Lower values of final cure conversions in the epoxy/thermoplastic blends indicate that thermoplastics hinder the cure reaction between the epoxy and the curing agent. Complete miscibility was observed in the uncured blends of epoxy/thermoplastics up to $120^{\circ}C$ but phase separations occurred in the early stages of the curing process at higher temperatures than $120^{\circ}C$. According to the rheological measurement results, a rise of G' and G" at the onset of phase separation is seen. A rise of G' and G" is not observed for neat epoxy system since no phase separation is seen during cure reaction. At the onset of phase separation the rheological behavior was influenced by the amount of thermoplastics in the epoxy/thermoplastic blends, and the onset of phase separation can be detected by rheological measurements.

• Journal of the Korean Ceramic Society
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• v.53 no.6
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• pp.635-640
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• 2016

Electrical behavior of AlN ceramics sintered with $Y_2O_3$ as a sintering aid has been investigated with respect to additional $TiO_2$ dopant. From the impedance spectroscopy, it was found that the grain and grain boundary conductivities have greatly decreased with addition of $TiO_2$ dopant. The $TiO_2$ dopant also increased the activation energy of the grain conductivity by about 0.37 eV; this increase was attributed to the formation of an associate between Al vacancies and Ti ions at the Al sites. Similarly, the electronic conductivity was reduced by $TiO_2$ addition. However, $TiO_2$ solubility in AlN grains was below the detection limit of typical EDX analysis. Grain boundary was clean, without liquid films, but did show yttrium segregation. The transference number of ions was close to 1, showing that AlN is a predominantly ionic conductor. Based on the observed results, the implications of using AlN applications as insulators have been discussed.

• Analytical Science and Technology
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• v.16 no.3
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• pp.198-205
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• 2003

The ammonium salt of nitrosophenylhydroxylamine, called cupferron, has been used not only as the ligand but also as an oxidizing agent for adsorptive catalytic stripping voltammetry (AdCtSV). Cyclic voltammetry was used for elucidating the electrochemical behavior of Os-cupferron complex in 1 mM phosphate buffer. The optimal conditions for osmium analysis were found to be 1 mM phosphate buffer solution (pH 6.0) containing 0.1 mM cupferron at scan rate of 100 mV/s. By using the plot of reduction peak currents of linear scan voltammograms vs. osmium concentration, the detection limit was $1.0{\times}10^{-7}M$.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1117-1127
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• 2017

With the advent of Internet of Things (IoT), the number of devices connected to Internet has rapidly increased, but the security for IoT is still vulnerable. It is difficult to integrate existing security technologies due to generating a large amount of traffic by using different protocols to use various IoT devices according to purposes and to operate in a low power environment. Therefore, in this paper, we propose a normal network behavior profiling method based on big data analysis techniques. The proposed method utilizes a Hadoop/Hive for Big Data analytics and an R for statistical computing. Also we verify the effectiveness of the proposed method through a simulation.