• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.93-100
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• 2017

Current internet has evolved from the sharing and efficiency aspects of information, it is still vulnerable to the fact that the Internet is not secure in terms of security and is not safe to secure of security mechanism. Repeating patches on continuous hacking are continuously demanding additional resources for network or system equipment, and consequently the costs continue to increase. Businesses and individuals alike are speeding up the damage caused by crime like of ransomware, not jusy simple attacks, and businesses and individuals need to respond to cyber security. In addition, the ongoing introduce of security device, and separate of networks for secure transmission of contents in the existing TCP/IP system, but it is still lacking in security. To complement the security implications of this existing TCP/IP Internet Protocol, we intend to propose a Secure Contents Transport System (SCTS) on the network using the CCN concept.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.3-3
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• 2004

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.91-97
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• 2021

The United States is responding to evolving cyberattacks through the Commercial Solutions for Classified Program (CSfC). Authorized safety evaluation and certification are being carried out so that US government agencies can quickly introduce civilian commercial security products into the national pavilion. Commercial security products registered in the CSfC process can be used by defense agencies through a rapid approval process. Defense agencies approve commercial security products without duplicate evaluation. Approved security products can reduce the time, cost, and cost of the approval process required to implement the defense information system. In this study, security control for 4 types of network security architecture MSC (Multi-Site Connectivity), MA (Mobile Access), Campus WLAN, and DAR (Data at Rest) proposed by the US National Security Agency (NSA) for introduction to national defense A detailed analysis was performed on the items.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Convergence Security Journal
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• v.16 no.7
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• pp.3-11
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• 2016

With repid development of optical networking technology, Software-Defined Network (SDN) and Network Function Virtualization (NFV), high performance networking service, collaboration platform that enables collaborative research globally, drastically National Research Network (NRN) including Internet Service has changed. Therefore we compared and analyzed several world-class NRNs and took a view of future development strategy of the NRN. Also we suggest high speed security environment in super high bandwidth network with 40Gbps and 100Gbps optical transmission technology, network separation of NRN with Science DMZ to support high performance network transmission for science big data, building security environment for last-mile in campus network that supports programmability of IDS using BRO framework.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.30-35
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• 2015

The most important factor within the wireless sensor network is to have effective network usage and increase the lifetime of the individual nodes in order to operate the wireless network more efficiently. Therefore, many routing protocols have been developed. The LEACH protocol presented by Wendi Hein Zelman, especially well known as a simple and efficient clustering based routing protocol. However, because LEACH protocol in an irregular network is the total data throughput efficiency dropped, the stability of the cluster is declined. Therefore, to increase the stability of the cluster head, in this paper, it proposes a stochastic cluster head selection method for improving the LEACH protocol. To this end, it proposes a SH-LEACH (Stochastic Cluster Head Selection Method-LEACH) that it is combined to the HEED and LEACH protocol and the proposed algorithm is verified through the simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.4
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• pp.340-344
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• 2004

In this paper, We collected the data of communication network construction in Sangmyeong University and analyzed them. Moreover using the analysis instrument of communication network traffic, We tested a process of it, and an efficiency of each equipment. We proved the traffic flow and the load quantity during the register for class. As a result We suggested a efficient communication network design - add and change some equipments in order to break up the traffic.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.473-476
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• 2008

USN(Ubiquotous Sensor Network) has been applied to various fields of industries such as logistics, environment management, traffic management, as well as IT industries including home network and telematics. Among the important techniques required to implement aforementioned applications, location management scheme is essential. In this paper, we proposed and implemented a new location measurement scheme based on RSSI of sensor node for campus car location management.