• Journal of Electrical Engineering and Technology
• /
• v.10 no.1
• /
• pp.401-407
• /
• 2015

The increase of image spam, a kind of spam in which the text message is embedded into attached image to defeat spam filtering technique, is a major problem of the current e-mail system. For nearly a decade, content based filtering using text classification or machine learning has been a major trend of anti-spam filtering system. Recently, spammers try to defeat anti-spam filter by many techniques. Text embedding into attached image is one of them. We proposed an ontology spam filters. However, the proposed system handles only text e-mail and the percentage of attached images is increasing sharply. The contribution of the paper is that we add image e-mail handling capability into the anti-spam filtering system keeping the advantages of the previous text based spam e-mail filtering system. Also, the proposed system gives a low false negative value, which means that user's valuable e-mail is rarely regarded as a spam e-mail.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.2
• /
• pp.105-109
• /
• 2006

In this paper, we constructed a two-phase 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 ham (non-spam) mail. The definite information is the mail sender's information, URL, a certain spam keyword list, and the less definite information is the word list 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 in the 2nd phase. According to our results the ham misclassification rate was reduced if more lexical information was used as features, and the spam misclassification rate was reduced when the concept codes were included in features as well.

• Journal of Korea Multimedia Society
• /
• v.9 no.2
• /
• pp.255-263
• /
• 2006

It is common that a junk mail has an inconsistency of mail addresses between those of the mail headers and the mail recipients. In addition, users easily know that an email is a junk or legitimate mail only by looking for the title of the email. In this paper, we tried to apply the filtering classifiers of mail address validation check and the combination method of title-content weighting to improve the performance of junk mail filtering system. In order to verify the effectiveness of the proposed method, we performed an experiment by applying them to Naive Bayesian classifier. The experiment includes the unit testing and the combination of the filtering techniques. As a result, we found that our method improved 11.6% of recall and 2.1% of precision that it contributed the enhancement of the junk mail filtering system.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.21-25
• /
• 2005

In this paper, we constructed a two-phase 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 mail. The definite information is the mail sender's information, URL, a certain spam list, and the less definite information is the word list 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 in the $2^{nd}$ phase. 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.

• Proceedings of the KIEE Conference
• /
• 2004.05a
• /
• pp.126-128
• /
• 2004

As e-mail is becoming increasingly important in every day life activity, mail users spend more and more time organizing and classifying the e-mails they receive into folder. Many existing recommendation systems or text classification are mostly focused on recommending the products for the commercial purposes or web documents. So this study aims to apply these application to e-mail more necessary to users. This paper suggests a dynamic recommendation agent system based on Rule Filtering Component recommending the relevant category to enable users directly to manage the optimum classification when a new e-mail is received as the effective method for E-Mail Management. Moreover we try to improve the accuracy as eliminating the limits of misclassification that can be key in classifying e-mails by category. While the existing Bayesian Learning Algorithm mostly uses the fixed threshold, we prove to improve the satisfaction of users as increasing the accuracy by changing the fixed threshold to the dynamic threshold. We designed main modules by rule filtering component for enhanced scalability and reusability of our system.

• The KIPS Transactions:PartB
• /
• v.10B no.7
• /
• pp.853-862
• /
• 2003

Many more use e-mail purely on a personal basis and the pool of e-mail users is growing daily. Also, the amount of mails, which are transmitted in electronic commerce, is getting more and more. Because of its convenience, a mass of spam mails is flooding everyday. And yet automated techniques for learning to filter e-mail have yet to significantly affect the e-mail market. This paper suggests Web Mail Filtering Agent for Personalized Classification, which automatically manages mails adjusting to the user. It is based on web mail, which can be logged in any time, any place and has no limitation in any system. In case new mails are received, it first makes some personal rules in use of the result of observation ; and based on the personal rules, it automatically classifies the mails into categories according to the contents of mails and saves the classified mails in the relevant folders or deletes the unnecessary mails and spam mails. And, we applied Bayesian Algorithm using Dynamic Threshold for our system's accuracy.

• Journal of IKEEE
• /
• v.10 no.1 s.18
• /
• pp.49-54
• /
• 2006

Recently, e-mail has become an important way of communications in IT societies, but it creates various social problems due to increase of spam mails. Even though many organizations and cooperation have been trying researches to develop spam mail blocking technologies, a lot of cost and system complexities are required because of varieties of spam blocking technologies. In this paper, we designed of the SMBC(Spam Mail Blocking Center) using the Fit FA(Filtering Algorithm) Finder. Fit-FA Finder that search and applises spam mail filtering algorithm of the most suitable confrontation according to type of spam mail. The system of spam mail filtering is decided performance of the system by procedure that spam filter is used. Go through designed Fit-FA Finder and reduced unnecessary filtering process and processing time and load than appointment order filter application way of existent spam mail interception system.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.2
• /
• pp.129-137
• /
• 2008

In this paper, we propose an improved Bayesian Filtering mechanism to reduce the False Positives that occurs in the existing Bayesian Filtering mechanism. In the existing Bayesian Filtering mechanism, the same Bayesian Filtering DB trained at the e-mail server is applied to each e-mail user. Also, the training method using receiving e-mails only could not provide the high quality of ham DB. Due to these problems, the existing Bayesian Filtering mechanism can produce the False Positives which misclassify the ham e-mails into the spam e-mails. In the proposed mechanism, the sending e-mails of the user are treated as the high quality of ham information, and are trained to the Bayesian ham DB automatically. In addition, by providing a different Bayesian DB to each e-mail user respectively, more efficient e-mail filtering service is possible. Our experiments show the improvement of filtering accuracy by 3.13%, compared to the existing Bayesian Filtering mechanism.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.17-20
• /
• 2005

In this work, we compared several feature selection methods in the field of spam mail filtering. The proposed fuzzy inference method outperforms information gain and chi squared test methods as a feature selection method in terms of error rate. In the case of junk mails, since the mail body has little text information, it provides insufficient hints to distinguish spam mails from legitimate ones. To address this problem, we follow hyperlinks contained in the email body, fetch contents of a remote web page, and extract hints from both original email body and fetched web pages. A two-phase approach is applied to filter spam mails in which definite hint is used first, and then less definite textual information is used. In our experiment, the proposed two-phase method achieved an improvement of recall by 32.4% on the average over the $1^{st}$ phase or the $2^{nd}$ phase only works.

• Convergence Security Journal
• /
• v.20 no.3
• /
• pp.61-69
• /
• 2020

Since spear-phishing mail attacks focus on a particular target persistently to collect and take advantage of information, it can incur severe damage to the target as a part of the intelligent and new attacks such as APT attacks and social engineering attacks. The usual spam filtering services can have limits in countering spear-phishing mail attacks because of different targets, goals, and methods. In this paper, we analyze mail security services of several enterprises hosted by midium and small-sized enterprises with relatively security vulnerabilities in order to see whether their services can effectively respond spear-phishing mail attacks. According to the analysis result, we can say that most of mail security hosting services lack in responding spear-phishing mail attacks by providing functions for mainly managing mails including spam mail. The analysis result can be used as basic data to extract the effective and systematic countermeasure.