• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.11-19
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• 2004

With the help of information technology the number of email users and personal home page owners are increasing. Marketers have much interest in using the email and personal home pages as a marketing promotion tool which can provide potential customers with messages they want to send. Marketers can facilitate the promotion efforts once if the profiles of potential customers' information can be databased by sending proper messages to the targeted market. Because of the merit of email and personal home page hotel firms are expected to adopt the information applications in their promotions for customers. This study proposes the Possibilities of email and personal home pages as a marketing promotion tool in the hotel industry and discusses problems to be overcome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Proceedings of the Korea Contents Association Conference
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• 2004.05a
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• pp.385-391
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• 2004

Today, mobile technology is developing very fast and people use internet through mobile phones. Using mobile phones, people use games, email, avatar and other services. We designed client/server modules in order to join email and avatar together. First two major server modules were designed, one of them is DB management module for avatar user information and the other is email management module. One the client side, we designed avatar image animation module and a module for showing the email contents. The purpose of the design is to reduce the performance overhead on the client side as mobile phones cannot be used as a high performance device. We propose and implement a new avatar based service using the SK-VM environment, that is different from the current avatar services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.45-54
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• 2011

Corporations use e-mail as their primary method for internal communication and business processes. By their nature, the e-mails are in general used for major business processes that contain large amounts of business information. When there is a critical event, such as Technology leakage, an e-mail message can become important evidence. However, as there is a high likelihood that a suspect will intentionally erase an e-mail message, the ability to recover deleted e-mail is very important. This pater analyzes the deleted e-mail item structure in files of various e-mail clients, and explains the possibility and methods of recovery.

• English Language & Literature Teaching
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• v.8 no.1
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• pp.125-141
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• 2002

This paper examines the pragmatic features of the thanking and apologizing moves which appear in requestive email of Korean speakers of English as a foreign language and American English native speakers. It is important for second language learners to behave appropriately in a target language when communicating with other English speakers who have different cultural backgrounds. The result of this study revealed the differences in the use of thanking and apologizing moves in the requestive email between Koreans and Americans. Koreans used fewer moves of thanking and more moves of apologizing than Americans in three different situations. Koreans' underuse of thanking which is a routine and formulaic expression for Americans could be a marked phenomenon to a recipient of the email in English bringing about a minus effect.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.378-384
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• 2005

In this paper, we describe a novel method of spam-filtering to improve the performance of conventional spam-filtering systems. Conventional systems filter emails by investigating words distribution in email headers or bodies. Nowadays, spammers begin making email accounts in web-based email service sites and sending emails as if they are not spams. Investigating the email accounts of those spams, we notice that there is a large difference between the automatically generated accounts and ordinaries. Based on that difference, incoming emails are classified into spam/non-spam classes. To classify emails from only account strings, we used decision trees, which have been generally used for conventional pattern classification problems. We collected about 2.15 million account strings from email service sites, and our account checker resulted in the accuracy of $96.3\%$. The previous filter system with the checker yielded the improved filtering performance.

• The Journal of Society for e-Business Studies
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• v.19 no.1
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• pp.119-129
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• 2014

The renowned Pareto rule of 80/20 has been challenged in the electronic marketplace with the emergence of long tail economy. Mass customization on top of the Internet infrastructure is expected to explain these changes of product concentration. In this paper, we empirically analyzed the micro-transactional data of a Groupon-like daily deal web site to identify the changes of product and customer concentration. The results show the long tail pattern aligned with the previous research on the e-commerce literature on the long tail. We find that the notification setting on email or SMS about daily deal influences the patterns of sales concentration. The information through email and SMS is expected to enable consumers to know about daily bargains and purchase the coupons eventually. However, the email notification for niche products results in the decreased sales while the SMS notification for overall product promotes overall products.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.31-38
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• 2009

To provide the privacy of a keyword, a public key encryption with keyword search(PEKS) firstly was propsed by Boneh et al. The PEKS scheme enables that an email sender sends an encrypted email with receiver's public key to an email server and a server can obtain the relation between the given encrypted email and an encrypted query generated by a receiver. In this email system, we easily consider the situation that a user sends the one identical encrypted email to multi-receiver like as group e-mail. Hwang and Lee proposed a searchable public key encryption considering multi-receivers. To reduce the size of transmission data and the server's computation is important issue in multi-receiver setting. In this paper, we propose an efficient searchable public key encryption for multi-receiver (mPEKS) which is more efficient and reduces the server's pairing computation.

• Proceedings of the Culinary Society of Korean Academy Conference
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• 2006.07a
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• pp.29-46
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• 2006

• Journal of IKEEE
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• v.25 no.4
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• pp.672-678
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• 2021

New malware continues to increase and become advanced by every year. Although various studies are going on executable files to diagnose malicious codes, it is difficult to detect attacks that internalize malicious code threats in emails by exploiting non-executable document files, malicious URLs, and malicious macros and JS in documents. In this paper, we introduce a method of analyzing malicious code for email security through proactive detection and blocking of malicious email attacks, and propose a method for determining whether a non-executable document file is malicious based on AI. Among various algorithms, an efficient machine learning modeling is choosed, and an ML workflow system to diagnose malicious code using Kubeflow is proposed.