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Yoo S.-K.,Attached Institute of ETRI | Karakoyunlu D.,Worcester Polytechnic Institute | Birand B.,Worcester Polytechnic Institute | Sunar B.,Worcester Polytechnic Institute
ACM Transactions on Reconfigurable Technology and Systems

A ring oscillator-based true-random number generator design (Rings design) was introduced in Sunar et al. [2007]. The design was rigorously analyzed under a simple mathematical model and its performance characteristics were established. In this article we focus on the practical aspects of the Rings design on a reconfigurable logic platform and determine their implications on the earlier analysis framework. We make recommendations for avoiding pitfalls in real-life implementations by considering ring interaction, transistor-level effects, narrow signal rejection, transmission line attenuation, and sampler bias. Furthermore, we present experimental results showing that changing operating conditions such as the power supply voltage or the operating temperature may affect the output quality when the signal is subsampled. Hence, an attacker may shift the operating point via a simple noninvasive influence and easily bias the TRNG output. Finally, we propose modifications to the design which significantly improve its robustness against attacks, alleviate implementation-related problems, and simultaneously improve its area, throughput, and power performance. © 2010 ACM. Source

Jung K.-Y.,Hanyang University | Ju S.,Attached Institute of ETRI | Teixeira F.L.,Ohio State University
IEEE Microwave and Wireless Components Letters

We develop a modal finite-difference time-domain (FDTD) method with a complex-frequency-shifted (CFS) perfectly matched layer (PML) to analyze magnetic photonic crystal (MPhC) waveguides. MPhCs are periodic structures with unit cell composed of two misaligned anisotropic dielectric layers and one ferromagnetic layer. Numerical results show that the proposed modal FDTD can reduce both memory and CPU costs by one order of magnitude or more compared to the conventional FDTD. © 2011 IEEE. Source

Shin S.,Attached Institute of ETRI | Lee S.,Technology Strategy Research Division | Kim H.,Kyungil University | Kim S.,Applied Technology Internet
Expert Systems with Applications

Recently, as damage caused by Internet threats has increased significantly, one of the major challenges is to accurately predict the period and severity of threats. In this study, a novel probabilistic approach is proposed effectively to forecast and detect network intrusions. It uses a Markov chain for probabilistic modeling of abnormal events in network systems. First, to define the network states, we perform K-means clustering, and then we introduce the concept of an outlier factor. Based on the defined states, the degree of abnormality of the incoming data is stochastically measured in real-time. The performance of the proposed approach is evaluated through experiments using the well-known DARPA 2000 data set and further analyzes. The proposed approach achieves high detection performance while representing the level of attacks in stages. In particular, our approach is shown to be very robust to training data sets and the number of states in the Markov model. © 2012 Elsevier Ltd. All rights reserved. Source

Jeon S.,Attached Institute of ETRI | Kim S.,Pohang University of Science and Technology | Yu H.,Pohang University of Science and Technology
Information Sciences

Watching TV programs at the scheduled airtime is difficult due to time differences between countries or personal circumstances. Not to be a victim of spoilers, people sometimes choose a self imposed isolation from civilization until they have seen their favorite program, such as to stay away from the Internet. However, smartphones allow people to habitually check the SNS messages posted by their friends to maintain their relationships. It leads to the problem of exposing spoilers about their favorite TV programs. To prevent a self imposed isolation from their friends, we need automatic method for detecting spoilers from TV program tweets. To the best of our knowledge, there have been two works that have addressed the spoiler detection task: (1) a keyword matching method and (2) a machine-learning method based on Latent Dirichlet Allocation (LDA). However, they were not designed for short texts as well as the real-world system. The keyword matching method incorrectly predicts most tweets as spoilers. Although the LDA-based method works well on large bodies of text, it fails to accurately detect spoilers from short texts such as Twitter. In this work, we introduce a simple and powerful method of spoiler detection based on four representative features, which are significant indicators of spoilers. To identify and utilize four features, we conduct a precise analysis on real-world tweet data, and we build an SVM-based prediction model based on the result. Using tweets about Dancing with the Stars, and the final of the 2014 World-Cup, we evaluate the effectiveness of the proposed methods on spoiler detection tasks. According to the result, our method achieves greater precision than the competitors while maintaining a comparable recall performance. At the same time, our method outperforms the competitors in terms of processing time, showing that our method is sufficiently lightweight for application to the web-browser. Furthermore, to reduce the labeling cost, we introduce a semi-supervised approach that automatically re-trains the prediction model based on a small amount of labeled data. The experimental results show that the semi-supervised approach delivers performance comparable to that of the previous model. © 2015 Published by Elsevier B.V. Source

Cho M.-H.,Attached Institute of ETRI
2012 IEEE International Conference on Wireless Information Technology and Systems, ICWITS 2012

I have developed eRTOS-USN which is a real-time operating system for ubiquitous sensor networks, and successfully ported it on the Nano24 (with atmega128L CPU) sensor nodes. eRTOS-USN is suitable for the low computing power, small memory embedded controllers used in USN applications. © 2012 IEEE. Source

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