• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.101-115
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• 2011

A botnet is a huge network of hacked zombie PCs. Recognizing the fact that the majority of email spam is sent out by botnets, a system that is capable of detecting botnets and zombie PCS will be designed in this study by analyzing email spam. In this study, spam data collected in "an email spam trail system", Korea's national spam collection system, were used for analysis. In this study, we classified the spam groups by the URLs or attached files, and we measured how much the group has the characteristics of botnet and how much the IPs have the characteristics of zombie PC. Through the simulation result in this study, we could extract 16,030 zombie suspected PCs for one hours and it was verified that email spam can provide considerably useful information in tracing zombie PCs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1267-1277
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• 2021

The phone number used for advertising messages and voices used as bait in the voice phishing crime access stage is being used to send out a large amount of illegal loan spam, so we want to quickly block it. In this paper, our system is designed to block the usage of the phone number by rapidly restricting the use of the voice spam phone number that conducts illegal loan spam and voice phishing, and at the same time sends continuous calls to the phone number to prevent smooth phone call connection. The proposed system is a representative collaboration model between an illegal spam reporting agency and an investigation agency. As a result of developing the system and applying it in practice, the number of reports of illegal loaned voice spam and text spam decreased by 1/3, respectively. We can prove the effectiveness of this system by confirming that.

• Journal of KIISE
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• v.44 no.4
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• pp.339-343
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• 2017

As the number of customers who write fake reviews is increasing, online shopping sites have difficulty in providing reliable reviews. Fake reviews are called review spam, and they are written to promote or defame the product. They directly affect sales volume of the product; therefore, it is important to detect review spam. Review spam detection methods suggested in prior researches were only based on an international site even though review spam is a widespread problem in domestic shopping sites. In this paper, we have presented new review features of the domestic shopping site NAVER, and we have applied the formerly introduced method to this site for performing an evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5120-5142
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• 2018

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.13-20
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• 2006

In this paper, we constructed a spam-mail filtering system based on the lexical and conceptual information. There are two kinds of information that can distinguish the spam mail from the legitimate mil. The definite information is the mail sender's information, URL, a certain spam keyword list, and the less definite information is the word lists and concept codes extracted from the mail body. We first classified the spam mail by using the definite information, and then used the less definite information. We used the lexical information and concept codes contained in the email body for SVM learning. According to our results the spam precision was increased if more lexical information was used as features, and the spam recall was increased when the concept codes were included in features as well.

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.89-94
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• 2007

Even though lots of research have been doing about spam mail blocking technologies and their systems, the emergence of spam mails of new types causes the spam mail filtering rate to decrease and the occurrences of false-positive mails to increase. Therefore, existing spam mail filtering algorithms suffer from increasing load to be processed and decreasing reliability in spam mail blocking systems due to the shortage of newly developed algorithms and their research. This paper presents the Fit-FA Finder which is able to select appropriate algorithms to be applied and their procedures, and the development of the SMBC platform. The Fit-FA Finder is developed and implemented in the SMBC platform in which recovering process based on privacy information is employed for false-positive mails

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.129-134
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• 2015

Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.445-456
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• 2017

In this paper, we propose a method to detect spam tweets containing unhealthy information by using an n-gram dictionary under limited labeling. Spam tweets that contain unhealthy information have a tendency to use similar words and sentences. Based on this characteristic, we show that spam tweets can be effectively detected by applying a Naive Bayesian classifier using n-gram dictionaries which are constructed from spam tweets and normal tweets. On the other hand, constructing an initial training set requires very high cost because a large amount of data flows in real time in a twitter. Therefore, there is a need for a spam detection method that can be applied in an environment where the initial training set is very small or non exist. To solve the problem, we propose a method to generate pseudo-labels by utilizing twitter's retweet function and use them for the configuration of the initial training set and the n-gram dictionary update. The results from various experiments using 1.3 million korean tweets collected from December 1, 2016 to December 7, 2016 prove that the proposed method has superior performance than the compared spam detection methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.7-12
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• 2014

It has good features for social networking with friends SNS is maintained. However, various enterprises, individuals invading the inconvenience spammers have exposure to a number of users to tweet spam. The study was conducted in the existing research on these spam tweets. However, the results showed a more accurate classification and detection is difficult because of the lack of precision and different causes. In this paper, we describe how to classify the characteristics of spammers, classification criteria. Also has a link rate and difference between followers and following, these features were present classification criteria for spammers account. This experiment was performed according to the criteria. Randomized trial of spam and non-spam accounts were selected and account type was conducted according to the criteria 68% of the link ratio of spam accounts. Followers / Following ratio was 27581.5. Non-spam accounts was 6.12%. Followers / Following ratio was 1.26.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11B
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• pp.730-735
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• 2005

While the E-mail is a important way of fast communication in these days. it is real that the E-mail is often misused as a commercial advertisement method and creates many social problems. Even though various filtering techniques for blocking spam mails have been developed, reliability of mail systems is decreased by misreading normal mails as spam mails, i.e. false-positive errors. In this paper, the SMBC(Spam Mail Blocking Center) platform employing spam mail recovery method based on privacy information is proposed and designed. The SMBC is designed in frame layer based on spam blocking system of proxy sewer and can be physically implemented in various topology so that flexible development with layered module is possible. Using privacy information makes the proposed SMBC platform minimize processing load and false-positive error rates so that it can improve mail system reliabilities.