• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.477-482
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• 2016

As being the advanced analytics indispensable on big data for agile decision-making and tactical planning in enterprises, distributed processing platforms, such as Hadoop and Spark which distribute and handle the large volume of data on multiple nodes, receive great attention in the field. In Spark platform stack, Spark SQL unveiled recently to make Spark able to support distributed processing framework based on SQL. However, Spark SQL cannot effectively handle advanced analytics that involves machine learning and graph processing in terms of iterative tasks and task allocations. Motivated by these issues, this paper proposes the design of SQL-based big data optimal processing engine and processing framework to support advanced analytics in Spark environments. Big data optimal processing engines copes with complex SQL queries that involves multiple parameters and join, aggregation and sorting operations in distributed/parallel manner and the proposing framework optimizes machine learning process in terms of relational operations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1121-1130
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• 2016

RSA cryptosystem is one of the most widely used public key cryptosystem. The security of RSA cryptosystem is based on hardness of factoring large number and hence there are ongoing attempt to factor RSA modulus. General Number Field Sieve (GNFS) is currently the fastest known method for factoring large numbers so that CADO-NFS - publicly well-known software that was used to factor RSA-704 - is also based on GNFS. However, one disadvantage is that CADO-NFS could not always select the optimal polynomial for given parameters. In this paper, we analyze CADO-NFS's polynomial selection stage. We propose modified polynomial selection using Chinese Remainder Theorem and Euclidean Distance. In this way, we can always select polynomial better than original version of CADO-NFS and expected to use for factoring RSA-1024.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.535-540
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• 2016

Coreference resolution is to identify mentions in documents and is to group co-referred mentions in the documents. It is an essential step for natural language processing applications such as information extraction, event tracking, and question-answering. Recently, various coreference resolution models based on ML (machine learning) have been proposed, As well-known, these ML-based models need large training data that are manually annotated with coreferred mention tags. Unfortunately, we cannot find usable open data for learning ML-based models in Korean. Therefore, we propose an efficient coreference resolution model that needs less training data than other ML-based models. The proposed model identifies co-referred mentions using random forests based on sieve-guided features. In the experiments with baseball news articles, the proposed model showed a better CoNLL F1-score of 0.6678 than other ML-based models.

• Annual Conference of KIPS
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• 2014.04a
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• pp.475-478
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• 2014

최근 인터넷이 발전함에 따라 월드와이드웹(World Wide Web) 기반의 웹 서비스가 급격한 발전을 이루었다. 또한 이 웹 서비스를 바탕으로 다양한 컨텐츠들과 이를 이용하는 사용자의 수도 함께 증가하였다. 그러나 이와 같은 웹 서비스의 보편화가 증대될수록 이를 악용하려는 사이버 범죄 또한 비례하여 증가하고 있다. 최근에는 공격자들이 스마트폰을 대상으로 악성코드를 전파하기 위한 방법으로 웹 서비스를 활용하기 시작하면서 웹 서비스의 보안에 대한 중요성이 더욱 강조되고 있다. 이러한 웹 서비스 보안의 필요성을 인지하고, 많은 사람들이 무료로 쉽게 웹 서비스 보안취약점을 진단 할 수 있도록 여러 오픈소스 기반의 보안 취약점 진단도구가 연구, 개발되고 있다. 하지만 웹 서비스의 보안약점을 진단하는 도구의 적합성 평가 및 기능 분류가 명확하지 않아서 진단도구를 선택하고 활용함에 있어 어려움이 따른다. 본 논문에서는 OWASP에서 위험도에 따라 선정한 웹 서비스의 보안 취약점 Top 10 항목과 소프트웨어 보안약점 진단가이드 등을 통해 웹 서비스 보안 취약점을 진단하는 도구에 대한 분석 기준을 제시한다. 이후 오픈소스로 공개된 테스트 기반 취약점 탐지도구와 소스 기반 취약점 진단도구들에 대해 제시한 기준을 이용하여 분석한다. 본 논문의 분석결과로 웹 서비스의 안전성을 평가하기 위해 활용할 수 있는 진단 도구에 대한 분석정보를 제공함으로써 보다 안전한 웹 서비스의 개발과 운영에 기여할 것으로 기대한다.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.369-375
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• 2009

Residential broadband networks (RBN) are quickly evolving from xDSL/Cable networks to FTTH services with lOOMbps bandwidth. With the high-speed Internet services, subscribers tend to use web, multimedia, game or file sharing applications. Therefore, performance of RBN is important to the quality of services for users. Typically, Internet service providers (ISPs) are providing their RBN performance results that are measured by subscribers with the dedicated measurement tools. However, they do not make their results public to people. In order to obtain the performance results measured by users, we have to induce a lot of subscribers, which will take a lot of cost and time. Monarch, that is used in this paper, could provide the 3rd party measurement method without the help of ISPs and subscribers. With the extensive experiments through Monarch, we have shown that 3rd party measurement results are not much different from those of user-initiated measurement. In addition, we have observed that bandwidth is around $80{\sim}90Mbps$ and RTT is less than 30ms in FTTH services provided by three major Korean ISPs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.643-650
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• 2008

If existing system need to expand security part, the security was established after paying much cost, processing of complicated installation and being patient with inconvenience at user's view because of closed structure. In this thesis, those defects could be overcome by using open security tools and constructing security server, which is firewall of 'bastion' form including proxy server, certification server and so on. Also each security object host comes to decide acceptance or denial where each packet comes from, then determines security level each hosts. Precisely it is possible choosing the packets from bastion host or following at the other policies. Although an intruder enter into inside directly, it is constructed safely because encryption algorithm is applied at communication with security object host. This thesis suggests more flexible, independent and open security system, which improves existing security through systematic linkage between system security and network security.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.11-20
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• 2009

Due to increasing frequency of yellow dust, not to mention high rate of precipitation and cloud formation in summer season of Korea, atmospheric correction of satellite remote sensing is necessary. This research analyzes the effect of atmospheric correction has on imagery classification by comparing DN distribution before and after atmospheric correction. The image used in the research is LANDSAT-5 TM. As for atmospheric correction module, commercial product ATCOR, FLAASH as well as COST model released on the internet, were used. The result of experiment shows that class separability increased in building areas.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.373-378
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• 2019

Since many programming sources are open online, problems with reckless plagiarism and copyrights are occurring. Among them, source codes produced by repeated authors may have unique fingerprints due to their programming characteristics. This paper identifies each author by learning from a Google Code Jam program source using deep neural network. In this case, the original creator's source is to be vectored using a pre-processing instrument such as predictive-based vector or frequency-based approach, TF-IDF, etc. and to identify the original program source by learning by using a deep neural network. In addition a language-independent learning system was constructed using a pre-processing machine and compared with other existing learning methods. Among them, models using TF-IDF and in-depth neural networks were found to perform better than those using other pre-processing or other learning methods.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.9-18
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• 2021

Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Fréchet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.539-544
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• 2021

The tactical data link is a military data network to improve the ability to recognize battlefield situations. The ROK military is promoting the tactical data link performance improvement programs. Tactical data link is essential to combine and integrate various platforms, sensor data, and command and control (C2) systems. Therefore, the research on related technical fields is required. However, the tactical data link has not disclosed detailed technical information due to the characteristics of military operation. In this paper, we propose a data-based automated analysis methodology using intellectual property information to understand the technology trend of tactical data link. In this paper, data related to intellectual property is automatically collected and pre-processed, and analyzed in terms of time series. In addition, the current status of each institution of patent technology information was generated, and the process of identifying key-researchers through network analysis was presented with providing results of our approach in this paper.