• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.909-912
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• 2005

다양한 정보보호체계가 운영되고 있지만, 방화벽과 침입탐지시스템이 가장 많이 운영되고 있는 실정에서, 본 논문에서는 방화벽 관리자의 차단로그 분석을 효율적으로 지원하면서, 방화벽에 의해 차단되어 침입탐지시스템이 탐지하지 못해 관리자가 지나칠 우려가 있는 공격행위를 방화벽을 통해 인지할 수 있는 방안을 구성했다. 이를 통해 관리자는 침입탐지시스템과 함께 네트워크를 통한 스캔 및 DOS 등의 공격을 방화벽을 통해 인지할 수 있어 안정적인 네트워크 운영이 가능하다.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.352-361
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• 2004

In this paper, we study how to achieve the maximum capacity under delay constraints for large mobile wireless networks. We develop a systematic methodology for studying this problem in the asymptotic region when the number of nodes n in the network is large. We first identify a number of key parameters for a large class of scheduling schemes, and investigate the inherent tradeoffs among the capacity, the delay, and these scheduling parameters. Based on these inherent tradeoffs, we are able to compute the upper bound on the maximum per-node capacity of a large mobile wireless network under given delay constraints. Further, in the process of proving the upper bound, we are able to identify the optimal values of the key scheduling parameters. Knowing these optimal values, we can then develop scheduling schemes that achieve the upper bound up to some logarithmic factor, which suggests that our upper bound is fairly tight. We have applied this methodology to both the i.i.d. mobility model and the random way-point mobility model. In both cases, our methodology allows us to develop new scheduling schemes that can achieve larger capacity than previous proposals under the same delay constraints. In particular, for the i.i.d. mobility model, our scheme can achieve (n-1/3/log3/2 n) per-node capacity with constant delay. This demonstrates that, under the i.i.d. mobility model, mobility increases the capacity even with constant delays. Our methodology can also be extended to incorporate additional scheduling constraints.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.127-135
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• 1995

The diffusion neural network forms a Gaussian distribution by transferring an excitation to the surround. A DOG(difference of two Gaussians) is obtained by the diffusion neural network. This type of the DOG, which can detect the intensity changes of an image, has the same shape as a LOG(Laplacian of a Gaussian:${\Delta}^2$G) and narrow band pass characteristics. In this paper we show that another type of the DOG which has a very narrow Gaussian for the excitatory and a very wide Gaussian for the inhibitory, can be formed by the diffusion process of this network, This type of the DOG has a wide band width in spatial frequency domain and can be used efficiently in detecting special type of edges.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.897-904
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• 2013

The recursive circulant network G(N,d) can be widely used in the design and implementation of parallel processing architectures. It consists of N identical nodes, each node is connected through bidirectional, point-to-point communication channels to different neighbors by jumping $d^i$, where $0{\leq}i{\leq}{\lceil}{\log}_dN{\rceil}$ - 1. In this paper, we investigate the routing of a message on $G(2^m,4)$, a special kind of RCN, that is key to the performance of this network. On $G(2^m,4)$ we would like to transmit k packets from a source node to k destination nodes simultaneously along paths on this network, the $i^{th}$ packet will be transmitted along the $i^{th}$ path, where $1{\leq}k{\leq}m-1$, $0{{\leq}}i{{\leq}}m-1$. In order for all packets to arrive at a destination node quickly and securely, we present an $O(m^4)$ routing algorithm on $G(2^m,4)$ for generating a set of one-to-many node-disjoint and nearly shortest paths, where each path is either shortest or nearly shortest and the total length of these paths is nearly minimum since the path is mainly determined by employing the Hungarian method.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.149-161
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• 2023

Internet of Things (IoT) is a relatively new concept that has gained immense popularity in a short period of time due to its wide applicability in making human life more convenient and automated. As an illustration: the development of smart homes, smart cities, etc. However, it is also accompanied by a substantial number of risks and flaws. IoT makes use of low-powered devices, so secure, less time-consuming and energy-intensive transmission (routing) of messages due to the limited availability of energy is one of the many and most significant concerns for IoT developers. The following paper presents a trust-based routing scenario for the Internet of Things (IoT) that exploits the past transmission record from the cupcarbon simulator's log files. Artificial Neural Network is used to quantify knowledge of trust, calculate the value of trust, and share this information with other network devices. As a human behavioural pattern, trust provides a superior method for making routing decisions. If there is a tie in the trust values and no other path is available, the remaining battery power is used to break the tie and make a forwarding decision; this is also seen as a more efficient use of the available resources. The proposed algorithm is observed to have superior energy consumption and routing decisions compared to conventional routing algorithms, and it improves the communication pattern.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.4
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• pp.278-288
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• 2024

According to International Association of Classification Societies (IACS) Unified Requirement (UR) E26, ships contracted for construction after July 1, 2024 should be designed, constructed, commissioned and operated taking into account of cyber security. In particular, ship network monitoring tools should be installed in accordance with requirement 4.3.1 in IACS UR E26. In this paper, we propose a Security Information and Event Management (SIEM) security policy model for ships as an effective threat detection method by analyzing the cyber security regulations and ship network status in the maritime domain. For this purpose, we derived the items managed in the SIEM from the maritime cyber security regulations such as those of International Maritime Organization (IMO) and IACS, and defined 14 detection policies considering the status of the ship network. We also presents the detection policy for non-expert crews to understand it, and occurrence conditions depending on the ship's network environment to minimize indiscriminate alarms. We expect that the results of this study will help improve the efficiency of ship SIEM to be installed in the future.

• Journal of Communications and Networks
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• v.10 no.4
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• pp.371-383
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• 2008

Application of network coding in wireless two-way relay channels (TWRC) has received much attention recently because its ability to improve throughput significantly. In traditional designs, network coding operates at upper layers above (including) the link layer and it requires the input packets to be correctly decoded. However, this requirement may limit the performance and application of network coding due to the unavoidable fading and noise in wireless networks. In this paper, we propose a new wireless network coding scheme for TWRC, which is referred to as soft network coding (SoftNC), where the relay nodes applies symbol-by-symbol soft decisions on the received signals from the two end nodes to come up with the network coded information to be forwarded. We do not assume further channel coding on top of SoftNC at the relay node (channel coding is assumed at the end nodes). According to measures of the soft information adopted, two kinds of SoftNC are proposed: amplify-and-forward SoftNC (AF-SoftNC) and soft-bit-forward SoftNC (SBF-SoftNC). We analyze the both the ergodic capacity and the outage capacity of the two SoftNC schemes. Specifically, analytical form approximations of the ergodic capacity and the outage capacity of the two schemes are given and validated. Numerical simulation shows that our SoftNC schemes can outperform the traditional network coding based two-way relay protocol, where channel decoding and re-encoding are used at the relay node. Notable is the fact that performance improvement is achieved using only simple symbol-level operations at the relay node.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.721-728
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• 2013

Big data is one of the most spotlighted technological trends in these days, enabling new methods to handle huge volume of complicated data for a broad range of applications. Real-time network traffic analysis essentially deals with big data, which is comprised of different types of log data from various sensors. To tackle this problem, in this paper, we devise a big data based platform, RENTAP, to detect and analyse malicious network traffic. Focused on military network environment such as closed network for C4I systems, leading big data based solutions are evaluated to verify which combination of the solutions is the best design for network traffic analysis platform. Based on the selected solutions, we provide detailed functional design of the suggested platform.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.85-92
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• 1992

In this paper we propose a diffusion neural network model. In this model, each excitatory and inhibitory neuron has the capability of diffusing external excitations. We show that this model can be used for the detection of intensity changes of an input image. The relations between the diffusion coefficient, the iteration number of diffusion, and the detected spatial frequency are analyzed. The calculation time is reduced than that of a LOG(a Laplacian of a Gaussian) method.