• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2012-2030
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• 2021

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2255-2281
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• 2021

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• Journal of Information Technology Applications and Management
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• v.28 no.2
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• pp.1-16
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• 2021

In recent years, as IT technologies such as cloud computing and mobile payment have evolved and Internet users have increased, the Internet financial market has become intelligent, mobile, and platformed. This study considers the impact of the psychological characteristics of platform systems and users on the performance of fintech platforms. The results of this study are as follows. Information quality affected trust and commitment, service quality affected commitment only, and system quality affected trust and commitment. The perceived risk affected trust and commitment, and the perceived benefit only affected trust and was shown to have an insignificant relationship with immersion. Trust has been shown to have a significant relationship with commitment, and both trust and commitment affected performance. In the validation of mediation effects, trust has shown a partially mediated effect between information quality, system quality, perceived risks, and perceived benefits and performance. There was no mediation effect between service quality and performance. Immersion has been shown to have a partial mediating effect between information quality, service quality, system quality, perceived risk and performance, and there is no mediating effect between perceived benefits and performance. This study showed what are the main factors that affect the performance of the fintech platform and will be used as a useful foundation for increasing the performance of the platform in the future.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4725-4747
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• 2020

Software defined networking (SDN) is considered to be one of the most promising paradigms in the future. To solve the scalability and performance problem that a single and centralized controller suffers from, the distributed multi-controller architecture is adopted, thus forms multi-domain SDN. In a multi-domain SDN network, it is of great importance to ensure a reliable control plane. In this paper, we focus on the reliability problem of multi-domain SDN against controller failure from perspectives of backup controller deployment and controller replication. We firstly propose a placement algorithm for backup controllers, which considers both the reliability and the cost factors. Then a controller replication mechanism based on shared data storage is proposed to solve the inconsistency between the active and standby controllers. We also propose a shared data storage layout method that considers both reliability and performance. Besides, a fault recovery and repair process is designed based on the controller backup and shared data storage mechanism. Simulations show that our approach can recover and repair controller failure. Evaluation results also show that the proposed backup controller placement approach is more effective than other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4748-4762
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• 2020

Recently, the COVID 19 pandemic has affected on our daily lives and society in many ways. Specifically, it has brought rapid changes in the working environment from office working to smart telecommuting. In addition, cloud computing technology and services not only provided ubiquitous access, but also led to a sharing of information, internal-external communication channels, telework, and innovative smart work for the business process. As a result, smart work services based on social cloud networking have spread to the public sector. However, existing academic research examining smart work merely remains to focus on the theoretical conceptualization or to deal with merely several examples of private views. Best practices of smart work services based on cloud computing technology in the public field rarely exists. Moreover, many studies have been differently measured the values of smart work for private and public sectors depending on organizational singularities. Therefore, the study aims to define new theoretical implications and to explore future business strategies and policy directions based on a technical working group's personal psychological subjectivity. The research applied Q methodology, and selected five public organizations in Korea, that they have adopted or currently plan to adopt some part of smart work services.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.79-88
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• 2022

Recently, securing stable supply of fresh food is deemed as one of the important tasks. Accordingly, now the presence of cold chain along with the needs of a comfortable and healthy life is growing as the online market expands and the contactless industry grows, however, cold chain is being studied only in the aspect of ground and sea transportation. And, due to global warming and strengthening global environmental regulations, we believe that it is necessary to convert the existing road-centered logistics system into a railway-centered logistics system, a low-carbon transportation means. Therefore, in this paper we calculated the maximum energy required by the reefer container as a basic research necessary for constructing the low temperature distribution and cold chain based on the reefer container railway, and conducted a study on methods of supplying the reefer container power utilizing 1. tramline, 2. battery, 3. generator. The results of this paper can be utilized as a foundational study for building a cold chain based on a reefer container dedicated to freight trains in the future.