• Information Systems Review
• /
• v.16 no.2
• /
• pp.1-23
• /
• 2014

With side effects such as Phishing and Spam using personal information in Social Network Service, there is a growing need for studies related to harmful behaviors such as the reason for privacy violation. As such, this study assumed privacy violation to be ethical decision, making behavior and used the Theory of Planned Behavior and Motivation Theory, which are mostly used in social science to identify the factors affecting privacy violation. The results suggested that the Perceived Enjoyment and Punishment used in motivation studies affected privacy violation behaviors, and that the factors of the Theory of Planned Behavior such as Attitude toward Privacy Violation, Subjective Norms of Privacy Violation, and Perceived Behavioral Control with regard to Privacy Violation significantly influenced the Intention to Privacy Violation. On the other hand, Perceived Curiosity and Subjective Norms of Privacy Violation did not affect the Intention to Privacy Violation. Therefore, this study confirmed that the Theory of Planned Behavior was appropriate to explain the Intention to Privacy Violation, and that the variables of the Motivation Theory generally influenced the Attitude toward Privacy Violation. This study was significant since it extended the scope of theoretical privacy study from users and victims centered to inflictor and applied the Extended Theory of Planned Behavior using the variables of the Motivation Theory in the study of Intention to Privacy Violation. From the practical aspect, it provided the ground for privacy education based on the fact that the Attitude toward Privacy Violation can be curbed when education on the Privacy Concerns, Perceived Enjoyment, and Punishment with regard to privacy is strengthened. It also cited the need for the punishment of privacy violation and the practical ground to amend the terms and conditions of user license and Personal Information Protection Act to provide policy support.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.12
• /
• pp.2878-2884
• /
• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location. Then, we propose a new tweet bot detection algorithm by using both an entropy based on geographic variable of each user and device information of each user. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• KIPS Transactions on Computer and Communication Systems
• /
• v.2 no.6
• /
• pp.279-290
• /
• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• Journal of KIISE
• /
• v.42 no.3
• /
• pp.367-378
• /
• 2015

This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.

• Food Science of Animal Resources
• /
• v.24 no.1
• /
• pp.50-56
• /
• 2004

The wienner sausage(natural casing, N), wienner sausage(collagen casing, C), frankfurter sausage(F) and can products[spam(S), luncheon meat(L), jangjorim(J)] were obtained from different Korean meat processing companies and investigated for their salinity, saccharinity, pH, moisture and fat content, meat color and sensory evaluation. In sausage products, the saccharinity percent ranged 4.9∼5.0 in N, 6.6∼8.0 in C, and 5.2∼6.5 in F. The salinity percent of C and F were slightly higher than that of N. The pH values of all sausage product were above 6.0. The L＊ values of N were ranged 49.8∼56.7, which were slightly lower than those of C and R The sausage with high content of crude fat and high L＊value earned the highest score in overall acceptability. In can products, saccharinity percentage was higher in J compared to the S and L. The salinity percentage of S was slightly higher than those of Land J. In meat color, L＊ and a＊ values were not different between S and L, although b＊ value of L was slightly higher than that of S and J. There were not significantly different among can products, however, the product containing low-salt had the highest score in overall acceptability.

• Information Systems Review
• /
• v.11 no.1
• /
• pp.49-67
• /
• 2009

E-mail marketing is the cheapest channel in target marketing. The channel works amazingly well for marketers who know how to use it. The e-mail marketers are able to integrate transactional and behavioral data to improve the targeting content of e-mail marketing campaigns. The cost in e-mail marketing is low and e-mail marketing makes no pollution. But, the e-mail response rate is lower than all the other channels. So, it is very hard for companies to increase their sales volumes, though the companies are ready to execute e-mail marketing campaigns on the side of computer systems. Marketers can send messages easily to target customers compared to other channels. But, the possibility to be read by the customers is low. Normal e-mails are continually devalued by spam mails. This study shows the influence of e-mail marketing to increase sales used by credit cards, on the basis of the real data promoted by A bank, in the Republic of Korea. The analysis on the traits of the respondent can help marketers to target customers. If additional studies on the response prediction model on the basis of traits of potential respondents are done, the targeting method to increase the effectiveness of e-mail marketing will be better structured and organized.

• Journal of Internet Computing and Services
• /
• v.18 no.6
• /
• pp.35-46
• /
• 2017

Recently, attackers using malicious code in cyber security have been increased by attaching malicious code to a mail and inducing the user to execute it. Especially, it is dangerous because it is easy to execute by attaching a document type file. The author analysis is a research area that is being studied in NLP (Neutral Language Process) and text mining, and it studies methods of analyzing authors by analyzing text sentences, texts, and documents in a specific language. In case of attack mail, it is created by the attacker. Therefore, by analyzing the contents of the mail and the attached document file and identifying the corresponding author, it is possible to discover more distinctive features from the normal mail and improve the detection accuracy. In this pager, we proposed IADA2(Intelligent Attack mail Detection based on Authorship Analysis) model for attack mail detection. The feature vector that can classify and detect attack mail from the features used in the existing machine learning based spam detection model and the features used in the author analysis of the document and the IADA2 detection model. We have improved the detection models of attack mails by simply detecting term features and extracted features that reflect the sequence characteristics of words by applying n-grams. Result of experiment show that the proposed method improves performance according to feature combinations, feature selection techniques, and appropriate models.

• The Journal of Engineering Research
• /
• v.4 no.1
• /
• pp.5-22
• /
• 2002

With the rapid advance of internet service and the corresponding migration of service environment from the text-based one to WWW (World Wide Web) environment, the number of internet users is growing rapidly due to its easy usage. Accordingly, usage of internet as services for sending electronic mails to the other party over the network is becoming increasingly prevalent. Web-based electronic mailing system is comprised of a server and a client. The former provides the users with e-mail accounts and services, while the latter serves as a user interface. In other words, it enables those public users who dos not own e-mail accounts on the existing mail server to have an access to the mailing service through the web. In this paper, we designed a effective web-based electronic mailing system which is based on the internet explorer and linux operating system, which overcomes limitations of the existing e-mail systems and meets the need of a cost-efficient alternative. Our electronic mailing system also supports the convenience of users through appropriate handling of preregistered spam e-mails and multiple e-mails, and this facilitates the development of a stable e-mail system by being able to avoiding the low system performance due to the bursty characteristics of e-mail messages and the increasing number of users

• Information Systems Review
• /
• v.20 no.1
• /
• pp.61-80
• /
• 2018

Individuals often receive smishing campaigns (mobile phishing messages), which they treat as spam. Thus, firms should understand how their customers distinguish their promotion messages from smishing. However, only a few studies examined this important issue. The present study employs the elaboration likelihood model to develop research hypotheses on the relationship between message cue and message credibility. The message cue in this study is classified as content cue, which is found in the content of promotion messages, and as leakage cue, which is found in peripheral information in the message. Leakage cue includes orthography (inclusion of special characters)and an abbreviated link sent by a faithless sender. We also propose that contextualization has a moderating effect on the relationship between content cue and credibility. We conducted a survey experiment to examine the effect of message cues on message credibility in the context of respondents receiving discount coupons through mobile messages. The result of data analysis based on 166 responses suggests that leakage cue had a negative effect on message credibility. A message with defective content cue has a marginally negative effect on message credibility. In particular, defective content cue in a high-contextual message has a strong negative impact on message credibility. This effect was not observed in low-contextual messages. Moreover, message credibility is significantly low regardless of the degree of contextualization if there is a leakage cue in the message. Our findings suggest that mobile promotion messages should be customized for message receivers and should have no leakage cues.

• The Journal of Society for e-Business Studies
• /
• v.17 no.4
• /
• pp.69-93
• /
• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).