• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2674-2697
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• 2019

Identity authentication is a crucial line of defense for network security, and passwords are still the mainstream of identity authentication. So far trawling password attacking has been extensively studied, but the research related with personal information is always sporadic. Probabilistic context-free grammar (PCFG) and Markov chain-based models perform greatly well in trawling guessing. In this paper we propose a systematic targeted attacking model based on structure partition and string reorganization by migrating the above two models to targeted attacking, denoted as TG-SPSR. In structure partition phase, besides dividing passwords to basic structure similar to PCFG, we additionally define a trajectory-based keyboard pattern in the basic grammar and introduce index bits to accurately characterize the position of special characters. Moreover, we also construct a BiLSTM recurrent neural network classifier to characterize the behavior of password reuse and modification after defining nine kinds of modification rules. Extensive experimental results indicate that in online attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 275%, and respectively outperforms its foremost counterparts, Personal-PCFG, TarGuess-I, by about 70% and 19%; In offline attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 90%, outperforms Personal-PCFG and TarGuess-I by 85% and 30%, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.3
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• pp.179-196
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• 1986

In this paper, the peformance of a statistical packet voice/data multiplexer is studied. In ths study we assume that in the packet voice/data multiplexer two separate finite queues are used for voice and data traffics, and that voice traffic gets priority over data. For the performance analysis we divide the output link of the multiplexer into a sequence of time slots. The voice signal is modeled as an (M+1) - state Markov process, M being the packet generation period in slots. As for the data traffic, it is modeled by a simple Poisson process. In our discrete time domain analysis, the queueing behavior of voice traffic is little affected by the data traffic since voice signal has priority over data. Therefore, we first analyze the queueing behavior of voice traffic, and then using the result, we study the queueing behavior of data traffic. For the packet voice multiplexer, both inpur state and voice buffer occupancy are formulated by a two-dimensional Markov chain. For the integrated voice/data multiplexer we use a three-dimensional Markov chain that represents the input voice state and the buffer occupancies of voice and data. With these models, the numerical results for the performance have been obtained by the Gauss-Seidel iteration method. The analytical results have been verified by computer simylation. From the results we have found that there exist tradeoffs among the number of voice users, output link capacity, voic queue size and overflow probability for the voice traffic, and also exist tradeoffs among traffic load, data queue size and oveflow probability for the data traffic. Also, there exists a tradeoff between the performance of voice and data traffics for given inpur traffics and link capacity. In addition, it has been found that the average queueing delay of data traffic is longer than the maximum buffer size, when the gain of time assignment speech interpolation(TASI) is more than two and the number of voice users is small.

• Fisheries and Aquatic Sciences
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• v.24 no.4
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• pp.139-152
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• 2021

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.11
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• pp.1199-1205
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• 1992

In this paper, a Korean word recognition method which usese Neural Network and Hidden Markov Models(HMM) is proposed to improve a recognition rate with a small amount of learning data. The method reduces the fluctuation due to personal differences which is a problem to a HMM recognition system. In this method, effective recognizer is designed by the complement of each recognition result of the Hidden Markov Models(HMM) and Neural Network. In order to evaluate this model, word recognition experiment is carried out for 28 cities which is DDD area names uttered by two male and a female in twenties. As a result of testing HMM with 8 state, codeword is 64, the recognition rate 91[%], as a result of testing Neural network(NN) with 64 codeword the recognition rate is 89[%]. Finally, as a result of testing NN-HMM with 64 codeword which the best condition in former tests, the recognition rate is 95[%].

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.643-658
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• 1998

In this paper we analyze the performance of a statistical ATM multiplexer with bursty input traffic and two thresholds in the buffer by using queueing model. Two priority levels are considered for source traffic which is modeled by Markov Modulated Poisson Process to represent the bursty characteristics. Service time distributions of two priority sources are assumed to be same and deterministic for ATM environment. The partial buffer sharing scheme with one threshold may experience a sensitive state change around the threshold. But the proposed multiplexer with two thresholds avoids this hysterical phenominon to improve the system operation.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.54-59
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• 2010

This paper suggests a new data link protocol with an adaptive frame length control scheme for wireless data networks which is subject to errors that occur with time variance. We analyze the proposed scheme under a two-state markov block interference(BI) model. Numerical results show that the proposed scheme can achieve high throughput performance for both dense and diffuse burst noise channels.

• Journal of Korean Society for Quality Management
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• v.10 no.1
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• pp.13-15
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• 1982

A device is said to have three states if it has one good state and two mutually exclusive failure modes ; e. g., in one failure mode, it operates when it should not, in the other it doesn't operate when it Should. Some examples of such device include a fluid flow valve, an automatic machine, and an explosive. A Markov model is developed to obtain the availability Function of a 2-unit standby redundant system of such devices.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.421-435
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• 2022

The necessity of estimating the time and cost required for tunnel construction has led to extensive research in this regard. Since geological conditions are significant factors in terms of time and cost of road tunnels, considering these conditions is crucial. Uncertainties about the geological conditions of a tunnel alignment cause difficulties in planning ahead of the required construction time and costs. In this paper, the continuous-space, discrete-state Markov process has been used to predict geological conditions. The Monte-Carlo (MC) simulation (MCS) method is employed to estimate the construction time and costs of a road tunnel project using the input data obtained from six tunneling expert questionnaires. In the first case, the input data obtained from each expert are individually considered and in the second case, they are simultaneously considered. Finally, a comparison of these two modes based on the technique presented in this article suggests considering views of several experts simultaneously to reduce uncertainties and ensure the results obtained for geological conditions and the construction time and costs.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.3
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• pp.59-74
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• 2002

This study investigates two basic operations of mobility management of PCNs (Personal Communication Networks), i.e., the location update and the paging of the mobile terminal. From the realistic consideration that a user either moves through several cells consecutively or stays in a cell with long time, we model the mobility pattern by introducing two types of CRT (Cell Residence Time). Mobility patterns of the mobile terminal are classified Into various ways by using the ratios of two types of CRT. Cost analysis is performed for distance-based and movement-based location update schemes combined with blanket polling paging and selective paging scheme. It is demonstrated that in a certain condition of mobility pattern and call arrival pattern, 2-state CRT model produces different optimal threshold and so, is more effective than IID ( Independently-Identically-Distributed) CRT model. An analytical model for the new CRT model is compact and easily extendable to the other location update schemes.

• Journal of the military operations research society of Korea
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• v.17 no.1
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• pp.1-24
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• 1991

This report concerns the study of deciding replacement requirements for 1/4ton truck in Korea. Two causes of replacement, accidental loss and wearout are considered in the replacement requirements model which was developed in Defence Logistics Agency. The model represents the state of 1/4 ton truck inventory over time as a finite Markov chain process. An accidental loss rate, yearly usage rates. wearout rates are used in conjuction with the current mileage distribution of the inventory to forecast replacement requirements in future time periods.