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Towards Choosing Authentication and Encryption
Seongwook Youn,Hyun-chong Cho 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.3
Sensor networks are composed of provide low powered, inexpensive distributed devices which can be deployed over enormous physical spaces. Coordination between sensor devices is required to achieve a common communication. In low cost, low power and short-range wireless environment, sensor networks cope with significant resource constraints. Security is one of main issues in wireless sensor networks because of potential adversaries. Several security protocols and models have been implemented for communication on computing devices but deployment these models and protocols into the sensor networks is not easy because of the resource constraints mentioned. Memory intensive encryption algorithms as well as high volume of packet transmission cannot be applied to sensor devices due to its low computational speed and memory. Deployment of sensor networks without security mechanism makes sensor nodes vulnerable to potential attacks. Therefore, attackers compromise the network to accept malicious sensor nodes as legitimate nodes. This paper provides the different security models as a metric, which can then be used to make pertinent security decisions for securing wireless sensor network communication.
Analyzing Impact of Bitcoin Features to Bitcoin Price via Machine Learning Techniques
Seongwook Youn(윤성욱) 한국정보과학회 2019 정보과학회 컴퓨팅의 실제 논문지 Vol.25 No.7
2009년에 처음 소개된 비트코인은 전 세계적으로 출시되어있는 암호 화폐들 중 가장 대중적이다. 출시 될 당시에 가치는 아주 낮았고, 다른 암호 화폐처럼 대중적이지도 않았다. 비트코인에 오늘날 많은 사람들이 관심을 가지고 있다. 비트코인은 일반 화폐와 교환될 수도 있고 지불 용도로 사용할 수도 있다. 비트코인은 많은 온라인 상점들과 온라인 서비스에서 통용되고 있다. 암호 화폐는 특정 당국에 의해 규정되지 않고 일반적인 수요와 공급에 따라 규정되지도 않는다. 비트코인은 최근에 상당한 성공을 이루었다. 비트코인 가격이 어느 주요한 요소들에 의해 결정되는지에 대한 호기심이 본 연구를 진행하게 되었다. 가장 대중적인 비트코인 디지털 월렛(www.blockchain.com) 으로부터 Blockchain Wallet API에 기반해서 데이터셋으로부터 특징들을 뽑았다. 머신러닝 기법으로 polynomial regression을 이용하여 특징들의 영향도를 계산하였다. 결과적으로 어떤 특징들이 비트코인 가격 변동과 연관이 깊은지 살펴봤다. Bitcoin (BTC ticker) is the most popular crypto-currency in the world, the first release of which took place in 2009. In this respect, in a release date for this currency the price was equal basically to none and was not considered as popular as other known crypto-currency available at the time. Today bitcoin is in high demand from users around the globe. It can be exchanged through special exchanges for ordinary money, or used directly as a means of payment for anything of choice by the users. Bitcoin is accepted by many of the largest online stores and online services for products, goods and services worldwide. Quotes of crypto currency are not regulated by any legislative or legal authorities, and therefore are considered fluid, whereby the value depends totally on the current natural demand and supply. Recently, bitcoin has achieved a great success in use within open global markets. By being motivated from a review of these factors, we decided to take a deep look into the main factors and features characteristic of bitcoin. In this project, we tried to take a vision regarding the use and impact of bitcoin features from a dataset based on Blockchain Wallet API, which is derived from one of the most popular bitcoin digital wallets – Blockchain Wallet API [1]. As a target, we set different phases for the analysis and gathering of relevant data from API. In these terms, for calculating the impact of feature we have used machine learning techniques; which included a pure linear regression and expended version of a linear regression-polynomial regression.
Two-stage DOA estimation method for low SNR signals in automotive radars
Seongwook Lee,Young-Jun Yoon,Jae-Eun Lee,Heonkyo Sim,Seong-Cheol Kim IET 2017 IET radar, sonar & navigation Vol.11 No.11
<P>In this study, the authors propose an effective two-stage direction-of-arrival (DOA) estimation method for low signal-to-noise (SNR) signals in automotive radar systems. When antenna elements in the array receive low SNR signals, the performance of subspace-based DOA estimation algorithms is degraded. Concerning this case, they propose an enhanced DOA estimation method that offers better angular resolution and estimation performance. The authors' proposed method is comprised of two stages. In the first stage, they roughly estimate the DOA using conventional subspace-based DOA estimation algorithms. Thereafter, the fine DOA estimation is performed in the next stage. The fine estimation includes a received signal calibration method using a priori information acquired from the previous stage. From simulation results, in terms of root mean square error and resolution probability, their proposed method exhibits a DOA estimation performance that is superior to that of the conventional method. Moreover, with actual measurement data, they verify that the proposed method can be applied to practical automotive radar systems.</P>
Improved Spam Filter via Handling of Text Embedded Image E-mail
Seongwook Youn,Hyun-chong Cho 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.1
The increase of image spam, a kind of spam in which the text message is embedded into attached image to defeat spam filtering technique, is a major problem of the current e-mail system. For nearly a decade, content based filtering using text classification or machine learning has been a major trend of anti-spam filtering system. Recently, spammers try to defeat anti-spam filter by many techniques. Text embedding into attached image is one of them. We proposed an ontology spam filters. However, the proposed system handles only text e-mail and the percentage of attached images is increasing sharply. The contribution of the paper is that we add image e-mail handling capability into the anti-spam filtering system keeping the advantages of the previous text based spam e-mail filtering system. Also, the proposed system gives a low false negative value, which means that user’s valuable e-mail is rarely regarded as a spam e-mail.
Soft-In Soft-Out DFE and Bi-Directional DFE
Seongwook Jeong,Jaekyun Moon IEEE 2011 IEEE TRANSACTIONS ON COMMUNICATIONS Vol.59 No.10
<P>We design a soft-in soft-out (SISO) decision feedback equalizer (DFE) that performs better than its linear counterpart in turbo equalizer (TE) setting. Unlike previously developed SISO-DFEs, the present DFE scheme relies on extrinsic information formulation that directly takes into account the error propagation effect. With this new approach, both error rate simulation and the extrinsic information transfer (EXIT) chart analysis indicate that the proposed SISO-DFE is superior to the well-known SISO linear equalizer (LE). This result is in contrast with the general understanding today that the error propagation effect of the DFE degrades the overall TE performance below that of the TE based on a LE. We also describe a new extrinsic information combining strategy involving the outputs of two DFEs running in opposite directions, that explores error correlation between the two sets of DFE outputs. When this method is combined with the new DFE extrinsic information formulation, the resulting 'bidirectional' turbo-DFE achieves excellent performance-complexity tradeoffs compared to the TE based on the BCJR algorithm or on the LE. Unlike turbo LE or turbo DFE, the turbo BiDFE's performance does not degrade significantly as the feedforward and feedback filter taps are constrained to be time-invariant.</P>