• Journal of Advanced Navigation Technology
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• v.15 no.3
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• pp.392-404
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• 2011

In this paper, we address the issue of joint detection schemes for uplink cellular system when base station cooperation is possible for multi-user detection in multi-cell scenario. The ZF, ML, MMSE and SIC detection are analyzed and evaluated as a conventional scheme. ML attains the optimal performance but the complexity increases exponentially, ZF/MMSE have simple structure but have poor detection performance and SIC has better performance but it has large complexity and potential of the error propagation. However, they need the increased decoder complexity as the number of iteration is increased. We propose a new joint ML and ZF/MMSE detection scheme, which combines the partial ML decoding and ZF/MMSE detection, in order to decrease the decoder complexity. Simulation results show that the proposed scheme attains same or a little bit better BER performance and expect reduced decoder complexity, specially in the case of large number of Base Station are cooperated each other.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.595-604
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• 2004

By the help of expansion of computer network and rapid growth of Internet, the information infrastructure is now able to provide a wide range of services. Especially open architecture - the inherent nature of Internet - has not only got in the way of offering QoS service, managing networks, but also made the users vulnerable to both the threat of backing and the issue of information leak. Thus, people recognized the importance of both taking active, prompt and real-time action against intrusion threat, and at the same time, analyzing the similar patterns of in-trusion already known. There are now many researches underway on Intrusion Detection System(IDS). The paper carries research on the in-trusion detection system which hired supervised learning algorithm and Fuzzy membership function especially with Neuro-Fuzzy model in order to improve its performance. It modifies tansigmoid transfer function of Neural Networks into fuzzy membership function, so that it can reduce the uncertainty of anomaly intrusion detection. Finally, the fuzzy logic suggested here has been applied to a network-based anomaly intrusion detection system, tested against intrusion data offered by DARPA 2000 Intrusion Data Sets, and proven that it overcomes the shortcomings that Anomaly Intrusion Detection usually has.

• Convergence Security Journal
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• v.13 no.1
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• pp.59-70
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• 2013

Along with the rapid development of the wireless network (Wireless Network) technology for secure wireless communications, security problems have emerged as an important issue. In order to operate the wireless network intrusion detection system detects the agent installed on each wireless node should be. Ad-hoc network structures scattered in the AP over a wireless network without the node is a structure that makes it possible to communicate to connect. Intrusion detection agent to be installed on the node, and the corresponding energy consumption occurs when the survival time is reduced. On a node that can monitor a lot of traffic in order to increase the effect of intrusion detection, an intrusion detection agent should be placed. Therefore, in this paper, by taking advantage of the structure of Ad-hoc wireless network, considering the maximum living time of the network, while at the same time, the effectiveness of intrusion detection and intrusion detection by proposing a plan for installing the agent. Also improve the system performance by reducing the network load on each network, a system designed for data aggregation to reduce data redundancy, network energy consumption by reducing.

• Interdisciplinary Bio Central
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• v.3 no.4
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• pp.14.1-14.9
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• 2011

Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.267-277
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• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.579-590
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• 2012

Local linear jump detection in a discontinuous regression function involves the choice of the bandwidth and the performance of a local linear jump detector depends heavily on the choice of the bandwidth. However, little attention has been paid to this important issue. In this paper we propose two fully data adaptive bandwidth selection methods for a local linear jump detector. The performance of the proposed methods are investigated through a simulation study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.860-863
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• 2005

Ad-Hoc networks are a new generation of networks offering unrestricted mobility without any underlying infrastructure. Primary applications of Ad-Hoc networks are in military, tractical and other security sensitive operations, where the environment is hostile. Hence, security is a critical issue. In this paper, we ahve identified certain misbehaviors caused by mallicious node for reactive routing protocol. We also discuss the intrusion detection and intrusion prevention model to prevent several identified attacks in the networks

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.289-291
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• 2003

Selective logging is currently a widely adopted management practice throughout the tropics. Monitoring of spatial extent and intensity of such logging is, therefore, becoming an important issue for sustainable management of forest. This study explores the possibility of using vegetation indices and Landsat 7 ETM+ image for this purpose. Two dataset acquired on 2002 and 2000 of Labanan concession area East Kalimantan, Indonesia were used. Three different vegetation indices (MSAVI, SAVI and NDVI) slicing and differentiating methods were tested. The results showed that the MSAVI is superior with overall accuracy of 77% and kappa 0.64.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.329-334
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• 2012

The distribution submarine cables are normally used for power supply at island, which are mostly installed in the southern coast of KOREA, and partially installed in the west coast and Jeju-Island. There are two way of submarine cable burying system, buried and unburied type. Since 2003, KEPCO is entirely being constructing the distribution submarine cable by buried type. In this case, 'burial depth' is key index for evaluating the suitability of the buried situation. Therefore, the measurement accuracy of 'burial depth' is a big issue for burying system in the distribution submarine cable. This paper demonstrates the measurement error of burial depth that is affected by electrical factor such as grounding type of submarine cable in case of magnetic field detection method, and indicates the method to reduce the measurement error in buried type of distribution submarine cable system.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.15-18
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• 2004

A high data rate Wireless Personal Area Networks (WPAN) system is a hot issue in wireless communication communities and being standardized. Multi-band Orthogonal Frequency Division Multiplexing (MB-OFDM) is one of the candidates for WPAN standard. In this paper, we discuss the PLCP (Physical Layer Convergence Protocol) structure for MB-OFDM. Also we evaluate the performance of two signal detection algorithms, which are the method of cross-correlation with the original preamble and the signed preamble. The latter has a low complexity with a little degradation.