• Journal of KIISE
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• v.42 no.8
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• pp.1049-1056
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• 2015

Runtime Intrusion Prevention Evaluator (RIPE), published in 2011, is a benchmark suite for evaluating mitigation techniques against 850 attack patterns using only buffer overflow. Since RIPE is built as a single process, defense and attack routines cannot help sharing process states and address space layouts when RIPE is tested. As a result, attack routines can access the memory space for defense routines without restriction. We separate RIPE into two independent processes of defense and attacks so that mitigations based on confidentiality such as address space layout randomization are properly evaluated. In addition, we add an execution mode to test robustness against brute force attacks. Finally, we extend RIPE by adding 38 attack forms to perform format string attacks and virtual table (vtable) hijacking attacks. The revised RIPE contributes to the diversification of attack patterns and precise evaluation of the effectiveness of mitigations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.91-97
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• 2021

A microserver is a type of a computing server, in which two or more CPU nodes are implemented on a separate computing board, and a plurality of computing boards are integrated on a main board. In building a cluster system, the microserver has advantages in several points such as energy efficiency, area occupied, and ease of management compared to the existing method of mounting legacy servers in multiple racks. In addition, since the microserver uses a fast interconnection network between CPU nodes, performance improvement for data transfers is expected. The proposed microserver can mount a total of 16 computing boards with 4 CPU nodes on the main board, and uses Serial-RapidIO (SRIO) as an interconnection network. In order to analyze the performance of the proposed microserver in terms of the interconnection network which is a core performance issue of the microserver, we compare and quantify the performance of commercial microservers. As a result of the test, it showed up to about 7 times higher bandwidth improvement when transmitting data using the interconnection network. In addition, with CloudSuite benchmark programs used in actual cloud computing, maximum 60% reduction in execution time was obtained compared to commercial microservers with similar CPU performance specification.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Korea Real Estate Review
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• v.28 no.3
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• pp.21-35
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• 2018

The recent performance of residential mortgages demonstrated how default risk operated separately from prepayment risk. In this study, we investigated the determinants of the borrowers' decisions pertaining to early termination through default from the mortgage performance data released by Freddie Mac, involving securitized mortgage loans from January 2011 to September 2013. We estimated a Cox-type, proportional hazard model with a single risk on fundamental factors associated with default options for individual mortgages. We proposed a mortgage default model that included two specifications of delinquency: one using a delinquency binary variable, while the other using a delinquency probability. We also compared the results obtained from two specifications with respect to goodness-of-fit proposed in the spirit of Vuong (1989) in both overlapping and nested models' cases. We found that a model with our proposed delinquency probability variable showed a statistically significant advantage compared to a benchmark model with delinquency dummy variables. We performed a default prediction power test based on the method proposed in Shumway (2001), and found a much stronger performance from the proposed model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.119-129
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• 2011

TCP-related Flooding attacks still dominate Distributed Denial of Service Attack. It is a great challenge to accurately detect the TCP flood attack in hish speed network. In this paper, we propose the NIC_Cookie logic implementation, which is a kind of security offload engine against TCP-related DDoS attacks, on network interface card. NIC_Cookie has robustness against DDoS attack itself and it is independent on server OS and external network configuration. It supports not IP-based response method but packet-level response, therefore it can handle attacks of NAT-based user group. We evaluate that the latency time of NIC_Cookie logics is $7{\times}10^{-6}$ seconds and we show 2Gbps wire-speed performance through a benchmark test.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.

• Wind and Structures
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• v.35 no.4
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• pp.269-285
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• 2022

The pressure-mitigating effects of a high-speed train passing through a tunnel with a partially reduced cross-section are investigated via the numerical approach. A compressible, three-dimensional RNG k-ε turbulence model and a hybrid mesh strategy are adopted to reproduce that event, which is validated by the moving model test. Three step-like tunnel forms and two additional transitions at the tunnel junction are proposed and their aerodynamic performance is compared and scrutinized with a constant cross-sectional tunnel as the benchmark. The results show that the tunnel step is unrelated to the pressure mitigation effects since the case of a double-step tunnel has no advantage in comparison to a single-step tunnel, but the excavated volume is an essential matter. The pressure peaks are reduced at different levels along with the increase of the excavated earth volume and the peaks are either fitted with power or logarithmic function relationships. In addition, the Arc and Oblique-transitions have very limited gaps, and their pressure curves are identical to each other, whereas the Rec-transition leads to relatively lower pressure peaks in C_Pmax, C_Pmin, and ΔC_P, with 5.2%, 4.0%, and 4.1% relieved compared with Oblique-transition. This study could provide guidance for the design of the novel railway tunnel.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.139-147
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• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Restorative Dentistry and Endodontics
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• v.45 no.4
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• pp.45.1-45.8
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• 2020

Objective: The aim of this study was to introduce a gelatin/bovine serum albumin (BSA) tissue standard, which provides dissolution properties identical to those of biological tissues. Further, the study evaluated whether the utilization of endodontic activating devices led to enhanced phantom dissolution rates. Materials and Methods: Bovine pulp tissue was obtained to determine a benchmark of tissue dissolution. The surface area and mass of samples were held constant while the ratio of gelatin and BSA were varied, ranging from 7.5% to 10% gelatin and 5% BSA. Each sample was placed in an individual test tube that was filled with an appropriate sodium hypochlorite solution for 1, 3, and 5 minutes, and then removed from the solution, blotted dry, and weighed again. The remaining tissue was calculated as the percent of initial tissue to determine the tissue dissolution rate. A radiopaque agent (sodium diatrizoate) and a fluorescent dye (methylene blue) were added to the phantom to allow easy quantification of phantom dissolution in a canal block model when activated using ultrasonic (EndoUltra) or sonic (EndoActivator) energy. Results: The 9% gelatin + 5% BSA phantom showed statistically equivalent dissolution to bovine pulp tissue at all time intervals. Furthermore, the EndoUltra yielded significantly more phantom dissolution in the canal block than the EndoActivator or syringe irrigation. Conclusions: Our phantom is comparable to biological tissue in terms of tissue dissolution and could be utilized for in vitro tests due to its injectability and detectability.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1310-1319
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• 2024

The reactor cavity cooling system (RCCS) is a passive reactor safety system commonly present in the designs of High-Temperature Gas-cooled Reactors (HTGR) that removes heat from the reactor pressure vessel by means of natural convection and radiation. It is one of the factors responsible for ensuring that the reactor does not melt down under any plausible accident scenario. For the simulation of accident scenarios, which are transient phenomena unfolding over a span of up to several days, intermediate fidelity methods and system codes must be employed to limit the models' execution time. These models can quantify radiation heat transfer well, but heat transfer caused by natural convection must be quantified with the use of correlations for the heat transfer coefficient. It is difficult to obtain reliable correlations for HTGR RCCS heat transfer coefficients experimentally due to such a system's size. They could, however, be obtained from high-fidelity steady-state simulations of RCCSs. The Rayleigh number in RCCSs is too high for using a Direct Numerical Simulation (DNS) technique; thus, a Reynolds-Averaged Navier-Stokes (RANS) approach must be employed. There are many RANS models, each performing best under different geometry and fluid flow conditions. To find the most suitable one for simulating an RCCS, the RANS models need to be validated. This work benchmarks various RANS models against three experiments performed on the HTTR RCCS Mockup by the Japanese Atomic Energy Agency (JAEA) in 1993. This facility is a 1/6 scale model of a vessel cooling system (VCS) for the High Temperature Engineering Test Reactor (HTTR), which is operated by JAEA. Multiple RANS models were evaluated on a simplified 2d-axisymmetric geometry. They were found to reproduce the experimental temperature profiles with errors of up to 22% for the lowest temperature benchmark and 15% for the higher temperature benchmarks. The results highlight that the pragmatic turbulence models need to be validated for high Rayleigh natural convection-driven flows and improved accordingly, more publicly available experimental data of RCCS resembling experiments is needed and indicate that a 2d-axisymmetric geometry approximation is likely insufficient to capture all the relevant phenomena in RCCS simulations.