• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.245-251
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• 2014

Mix network plays a key role in electronic voting to preserve anonymity and lots of mixnet schemes have been proposed so far. However, they requires complex and costly zero-knowledge proofs to provide their correct mixing operations. In 2010, Seb$\acute{e}$ et al. proposed an efficient and lightweight mixnet scheme based on a cryptographic secure hash function instead of zero-knowledge proofs. In this paper, we present a more efficient and faster mixnet scheme than Seb$\acute{e}$ et al.'s scheme under the same assumption. Also, our scheme is secure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7B
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• pp.744-750
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• 2009

In this paper, the unified Markov modeling of hardware and software for AVTMR(AlI Voting Triple Modular Redundancy) system is proposed and the dependability is analyzed. In hardware case, a failure rate is fixed to no time varying parameter. But, in software case, failure rate is applied with time varying parameter. Especially, the dependability(Reliability, Availability, Maintainability, Safety) of software is analyzed with G-O/NHPP for Markov modeling. The dependability of single and AVTMR system is analyzed and simulated with a unified Markov modeling method, and the characteristic of each system is compared accroding to failure rate. This kind of fault tolerat system can be applied to an airplane and life critical system to meet the requirement for a specific requirement.

• Journal of the Korea Society for Simulation
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• v.16 no.1
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• pp.59-68
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• 2007

IT (Intrusion Tolerant) technology is for guaranteeing the availability of service for certain amount time against the attacks which couldn't be prevented by the currently deployed information security countermeasures. IT (Intrusion Tolerant) technology mainly makes use of the replication of service and system fur enhancing availability, and voting scheme and GMP (Croup Management Protocol) are used for the correctness of service. This paper presents a scheme to analyze dependability of IT (Intrusion Tolerant) technology through probabilistic and simulation method. Using suggested analysis scheme, we can analyze the robustness and make a sensible trade-offs in of IT (Intrusion Tolerant) technology.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.232-232
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• 1995

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.29-38
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• 2018

In this paper, we propose an automatic segmentation method of meniscus in knee MR images by automatic meniscus localization, multi-atlas-based locally-weighted voting, and patch-based edge feature classification. First, after segmenting the bone and knee articular cartilage, the volume of interest of the meniscus is automatically localized. Second, the meniscus is segmented by multi-atlas-based locally-weighted voting taking into account the weights of shape and intensity distribution in the volume of interest of the meniscus. Finally, to remove leakage to the collateral ligaments with similar intensity, meniscus is refined using patch-based edge feature classification considering shape and distance weights. Dice similarity coefficient between proposed method and manual segmentation were 80.13% of medial meniscus and 80.81 % for lateral meniscus, and showed better results of 7.25% for medial meniscus and 1.31% for lateral meniscus compared to the multi-atlas-based locally-weighted voting.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Journal of The Korean Association of Information Education
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• v.10 no.1
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• pp.119-127
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• 2006

This paper is designed to search the new alternatives to supplement the inconvenience of the traditional written ballot method which is executed every early semester to select the class board at the school. It is prepared on the based the results of the design and implement of the wired/wireless inter-working mobile electronic voting system where the students can participate in the real-time class board selection and the decision-making utilizing the mobile phones, PDA and PC they commonly have. It is time when we should consider introducing the electronic voting system, to minimize the students' inconvenience and the subsequent missing class, having to wait in the long line in the designated place to select the class board at every election season. This system enables the students to participate wherever they are other than the common place as well. Further, this research will provide the opportunity to think over the new school election culture in line with the age of Ubiquitous, as well as the useful means in the field to promote the active participation of the parents and students in the students' self-administration, decision-making necessary at the schools.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.17-25
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• 2013

TIn this paper, we propose a model automatically scoring a student's answer for a descriptive problem by using a voting method. Considering the model construction cost, the proposed model does not separately construct the automatic scoring model per problem type. In order to utilize features useful for automatically scoring the descriptive answers, the proposed model extracts feature values from the results, generated by comparing the student's answer with the answer sheet. For the purpose of improving the precision of the scoring result, the proposed model collects the scoring results classified by a few machine learning based classifiers, and unanimously selects the scoring result as the final result. Experimental results show that the single machine learning based classifier C4.5 takes 83.00% on precision while the proposed model improve the precision up to 90.57% by using three machine learning based classifiers C4.5, ME, and SVM.

• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.300-308
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• 2001

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.