A Host Protection Framework Against Unauthorized Access for Ensuring Network Survivability

Abstract. Currently, the major focus on the network security is securing individual components as well as preventing unauthorized access to network services. Ironically, Address Resolution Protocol (ARP) poisoning and spoofing techniques can be used to prohibit unauthorized network access and resource modifications. The protecting ARP which relies on hosts caching reply messages can be the primary method in obstructing the misuse of the network. This paper proposes a network service access control framework, which provides a comprehensive, hostby-host perspective on IP (Internet Protocol) over Ethernet networks security. We will also show how this framework can be applied to network elements including detecting, correcting, and preventing security vulnerabilities

Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

Recent developments in artificial intelligence (AI) have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology

Patent data analysis using functional count data model

Technology is an important cause of social change. So many researchers have studied on diverse methods for technology analysis. Patent analysis has been proposed in many studies for technical analysis. They extracted technological keywords and codes from patent documents and analyzed them using statistics and machine learning. One of the problems in the existing studies was the patent analysis that did not consider the time factor. However, time is a factor to be considered in technology analysis. Because technology has evolved over time, in this paper, we study and propose a new technology analysis method considering time factor. We analyze patent data to understand technological structure of company, because patent contains most of information about developed technology. A lot of studies on technology analysis using patent data have been published in various areas. Many of them used extracted technological keywords from patent documents for patent analysis. They did not consider time factor to build technology analysis models, but we know technology changes over time. So we propose a technology analysis method using functional data analysis as a patent analysis considering time factor. We select Apple technology for our case study. With the patent data of Apple over time, we investigate on the technological structure of Apple and its technological evolution through high-dimensional visualization using harmonic components generated by functional data analysis. In addition, by employing the count data regression models of Poisson, negative binomial and hurdle Poisson, we examine the relationships among highest frequency keywords based on the visual outputs in functional data analysis. The practical implication of this paper is that it can be applied more effectively than the existing studies of technology analysis by considering time factor. This research contributes to technology forecasting for understanding the social changing. We can develop a more efficient research and development plan to improve the technological competition. The originality of this research is to consider time factor in technology analysis based on patent data. In this paper, we used the functional data analysis to model trends of technology keywords over time. Using the results of the technology analysis of this study considering the time, the company will be able to understand the social change and thereby improve its technological competitiveness in the market

A Novel Forecasting Methodology for Sustainable Management of Defense Technology

A dynamic methodology for sustainable management of defense technology is proposed to overcome the limitations of the static methodology, which involves comparative analysis based on the criterion of the highest technology level and has limitations for time series analysis, because the country with the highest level undergoes technical changes over time. To address these limitations, this study applies a technology growth model for a dynamic analysis of the Delphi result. An effective method using patents is also proposed to verify and adjust the analysis results. First, technology levels of the present and future are examined by the Delphi technique, and the growth curve is extracted based on the technology growth model. Second, the technology growth curve based on patents is extracted using the annual number of unexamined and registered patents related to the technology. Lastly, the statistical significance of the two growth curves is examined using regression analysis. Then the growth curves are adjusted by the rate of increase in patents. This methodology could provide dynamic technology level data to facilitate sustainable management of defense technology. The results could be useful to research institutions, as they establish strategies for securing technologies in defense or private domains

Application of an Internet of Medical Things (IoMT) to Communications in a Hospital Environment

IoT technology is used in various industries, including the manufacturing, energy, finance, education, transportation, smart home, and medical fields. In the medical field, IoT applications can provide high-quality medical services through the efficient management of patients and mobile assets in hospitals. In this paper, we introduce an IoT system to the medical field using Sigfox, a low-power communication network for indoor location monitoring used as a hospital network. A proof-of-concept (PoC) was implemented to evaluate the effectiveness of medical device and patient safety management. Specific requirements should be considered when applying the IoMT system in a hospital environment. In this study, the location and temperature of various targets sending signals to the monitoring system using three different networks (Sigfox, Hospital and Non-Hospital) were collected and compared with true data, the average accuracy of which were 69.2%, 72.5%, and 83.3%, respectively. This paper shows the significance in the application of an IoMT using the Sigfox network in a hospital setting in Korea compared with existing hospital networks