• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1263-1267
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• 2007

A more complete two-dimensional vertical numerical model has been developed to describe the saltwater intrusion in an estuary. The model is based on the previous studies in order to obtain a better accuracy. The non-linear terms of the governing equations are analyzed and the $\sigma$-coordinate system is employed in the vertical direction with full transformation which is recently issued in several studies because numerical errors can be generated during the coordinate transformation of the diffusion term. The advection terms of the governing equations are discretized by an upwind scheme in second-order of accuracy. By employing an explicit scheme for the longitudinal direction and an implicit scheme for the vertical direction, the numerical model is free from the restriction of temporal step size caused by a relatively small grid ratio. In previous researches, some terms induced from the transformation have been intentionally excluded since they are asked the complicate discretization of the numerical model. However, the lack of these terms introduces significant errors during the numerical simulation of scalar transport problems, such as saltwater intrusion and sediment transport in an estuary. The numerical accuracy attributable to the full transformation is verified by comparing results with a previous model in a simply sloped topography. The numerical model is applied to the Han River estuary. Very reasonable agreements for salinity intrusion are observed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2171-2180
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• 2011

Security is a significant concern for many sensor network applications. Intrusion detection is one method of defending against attacks. However, standard intrusion detection techniques are not suitable for sensor networks with limited resources. In this paper, propose a new method for selecting and managing the detect nodes in IDS(intrusion detection system) for anomaly detection in sensor networks and the node scheduling technique for maximizing the IDS's lifetime. Using the genetic algorithm, developed the solutions for suggested optimization equation and verify the effectiveness of proposed methods by simulations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.35-43
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• 2018

MANET composed of only wireless nodes is increasingly utilized in various fields. However, it is exposed to many security vulnerabilities because it doesn't have any infrastructure and transmits data by using multi-hop method. Therefore, MANET should be applied the intrusion detection technique that can detect efficiently malicious nodes and decrease impacts of various attacks. In this paper, we propose a distributed intrusion detection technique that can detect the various attacks while improving the efficiency of attack detection and reducing the false positive rate. The proposed technique uses the cluster structure to manage the information in the center and monitor the traffic of their neighbor nodes directly in all nodes. We use three parameters for attack detection. We also applied an efficient authentication technique using only key exchange without the help of CA in order to provide integrity when exchanging information between cluster heads. This makes it possible to free the forgery of information about trust information of the nodes and attack nodes. The superiority of the proposed technique can be confirmed through comparative experiments with existing intrusion detection techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• Convergence Security Journal
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• v.5 no.2
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• pp.9-17
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• 2005

Recently It's difficult to deal with about variety of attack. And Simple Security management have a problem. It is that they don't develop system measuring their system envoirment and have efficient attack detector, countermeasure organization about large network. Therefore, need model about enterprise management of various security system and intrusion detection of each systems and response. In this paper, improve PBNM structure that manage wide network resources and presented suitable model in intrusion detection and response of security system. Also, designed policy-based enterprise security management system for effective intrusion detection and response by applying presented model to enterprise security management system.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.103-113
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• 2003

Program Behavior Intrusion Detection Technique analyses system calls that called by daemon program or root authority, constructs profiles. and detectes anomaly intrusions effectively. Anomaly detections using system calls are detected only anomaly processes. But this has a Problem that doesn't detect affected various Part by anomaly processes. To improve this problem, the relation among system calls of processes is represented by bayesian probability values. Application behavior profiling by Bayesian Network supports anomaly intrusion informations . This paper overcomes the Problems of various intrusion detection models we Propose effective intrusion detection technique using Bayesian Networks. we have profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions we had simulation by proposed normal behavior profiling technique using UNM data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.666-668
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• 2013

Wireless network environment is spreading due to the increase of using mobile devices, causing wireless network abuse. Network security and intrusion detection have been paid attention to wireless as well as wired existing and studied actively Snort-based intrusion detection system (Intrusion Detection System) is a proven open source system which is widely used for the detection of malicious activity in the existing wired network. Snort Wireless has been developed in order to enable the 802.11 wireless detection feature. In this paper, Snort Wireless Rule is analyzed. Based on the results of the analysis, present the traveling direction of future research.

• The Journal of the Petrological Society of Korea
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• v.5 no.2
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• pp.161-167
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• 1996

The CSD (crystal size distribution) of diopside crystals in the calc-silicate hornfels of the Hwanggangni Formation intruded by the Cretaceous Daeyasan granite shows the patterns of continuous nucleation and growth. There is correlation between the distance from the intrusion contact and the slopes from the linear part of log(population density) vs. size diagrams. In the log(population density) vs. size diagrams of the samples systematically collected from the intrusion contact, two different groups are recognized; the slopes for the samples near the intrusion contact (horizontal distance from the contact less than 50m) are gentler (1500$cm^{-1}$) than those for the samples away from the intrusion contact (2500$cm^{-1}$, distance from the contact greater than 100 m). These differences may reflect the differences in growth rates and crystallization time, or the differences in diopside-forming reactions. All of the log(population density) vs. size diagrams show depletion of smaller crystals. The observed depletion may be due to Ostwald ripening or the changes in nucleation rates as the reactant phases diminishes. Similar grouping is also possible for the observed degree of depletion of smaller crystals; the depletion decreases with increasing distance from the intrusion contact, suggesting temperature-dependent rates of Ostwald ripening.

• Convergence Security Journal
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• v.7 no.4
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• pp.93-105
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• 2007

A digital forensic technology and tools are used much in the rapidly increased cyber intrusion accident investigation. But, almost the identification and gathering tools of digital forensic evidence are very difficultly integrated and simply poor-skill. Thereby, Important digital evidences at intrusion accident investigation of public institution and a private enterprise can be omitted or demaged. In this paper, therefore, we refer to 'The digital forensic tool for identification and gathering evidence' based only Window OS by using 'Log Parser', discuss the methodology for the identification and gathering of digital forensic evidence by cyber intrusion accident types.