• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8B
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• pp.630-636
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• 2012

In this paper, we propose an improved Multi Gray-Leveling scheme which reduces the problems of the existing Multi Gray-Leveling scheme suggested as a way of prevention against call spam in VoIP environment. The existing scheme having two different time period distinguishes the possibility of call spam by checking the call interval, so that it prevents the spammer's avoidance controlling the call interval. This is the strength of the existing one but it can misunderstand the normal user as a spammer due to taking long term time period. To solve this problem, this paper proposes the upgrade scheme which utilizes the receiver's action pattern as well as the caller's action pattern. It has such a good strength that can do gray leveling via the collected information in the database of VoIP service provider without user's direct involvement. Hence it can be a very effective way of VoIP spam detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.129-137
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• 2008

As the use of the internet increases, the distribution of spam mail has also vastly increased. The email's main use was for the exchange of information, however, currently it is being more frequently used for advertisement and malware distribution. This is a serious problem because it consumes a large amount of the limited internet resources. Furthermore, an extensive amount of computer, network and human resources are consumed to prevent it. As a result much research is being done to prevent and filter spam. Currently, research is being done on readable sentences which do not use proper grammar. This type of spam can not be classified by previous vocabulary analysis or document classification methods. This paper proposes a method to filter spam by using the subject of the mail and N-GRAM for indexing and Bayesian, SVM algorithms for classification.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.95-105
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• 2009

Spam call is one of the main security threat in VoIP services. In this paper, we have designed simulation model for performance evaluation of VoIP spam defense mechanism. The simulation model has functions for performance evaluation such as calls generation and input/output comparison. Four representative caller models have been developed for performance evaluation and each model has its own characteristics as statistical parameters. The target mechanism of performance evaluation is SPIT(Spam over Internet Telephony) level decision algorithm, and we have derived SPIT levels of caller models. The performance evaluation model is designed using the DEVS formalism and DEVSJAVA$^{TM}$ is exploited for development and execution of simulation models.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.163-170
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• 2006

With rapidly developing Internet applications, an e-mail has been considered as one of the most popular methods for exchanging information. The e-mail, however, has a serious problem that users ran receive a lot of unwanted e-mails, what we called, spam mails, which cause big problems economically as well as socially. In order to block and filter out the spam mails, many researchers and companies have performed many sorts of research on spam filtering. In general, users of e-mail have different criteria on deciding if an e-mail is spam or not. Furthermore, in e-mail client systems, users do different actions according to a spam mail or not. In this paper, we propose a mail filtering system using such user actions. The proposed system consists of two steps: One is an action inference step to draw user actions from an e-mail and the other is a mail classification step to decide if the e-mail is spam or not. All the two steps use incremental learning, of which an algorithm is IB2 of TiMBL. To evaluate the proposed system, we collect 12,000 mails of 12 persons. The accuracy is $81{\sim}93%$ according to each person. The proposed system outperforms, at about 14% on the average, a system that does not use any information about user actions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2881-2894
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• 2018

Cyber attacks are increasing continuously. On average about one million malicious codes appear every day, and attacks are expanding gradually to IT convergence services (e.g. vehicles and television) and social infrastructure (nuclear energy, power, water, etc.), as well as cyberspace. Analysis of large-scale cyber incidents has revealed that most attacks are started by PCs infected with malicious code. This paper proposes a method of detecting an attack IP automatically by analyzing the characteristics of the e-mail transfer path, which cannot be manipulated by the attacker. In particular, we developed a system based on the proposed model, and operated it for more than four months, and then detected 1,750,000 attack IPs by analyzing 22,570,000 spam e-mails in a commercial environment. A detected attack IP can be used to remove spam e-mails by linking it with the cyber removal system, or to block spam e-mails by linking it with the RBL(Real-time Blocking List) system. In addition, the developed system is expected to play a positive role in preventing cyber attacks, as it can detect a large number of attack IPs when linked with the portal site.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.465-468
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• 2003

E-mail, one of the oldest services in internet becomes very important and essential way to communicate with development of internet. Due to E-mail has a property which is not complete for security, sometimes it is used for purpose of commercial or bad things, therefore it becomes the latest problem to keep off a Spam-mail and commercial advertising E-mail, many ways to keep off were perposed for it. In this paper, I explained how to sort and keep off these Spam-mail and commercial advertising E-mail with three way, prevention by server level, prevention by construction of network level, prevention by client level. we designed a prevention system for Spam-mail and implemented it by Visual Basic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.211-214
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• 2003

The problem of unsolicited e-mail has been increasing for years, so many researchers has studied about spam filtering and prevention. In this article, we proposed a faster spam prevention algorithm based on userID instead of full email address. But there are 2% of false-negatives by userID. In this case, we store those domains in a DB and filter them out. The proposed algorithm requires small DB and 3.7 times faster than the e-mail address comparison algorithm. We implemented this algorithm using SPRSW(Spam Prevention using Replay Secrete Words) to register userID automatically in userID DB.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.310-315
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• 2007

When a mail was given to users, each user's response could be different according to his or her preference. This paper presents a solution for this situation by constructing a u!;or preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or non-spam in a meaningful way. We also suggest a nor rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.413-417
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• 2002

E-mail was very particular way of communication in the past, but it becomes one of daily communication methods now. Due to E-mail has a property which is not complete for security, sometimes it is used for purpose of commercial or badthings, therefore it becomes the latest problem to keep off a Spam-mail and commercial advertising E-mail, many ways to keep off were perposed for it. In this paper, I explained how to sort and keep off these Spam-mail and commercial advertising E-mail with three way, prevention by server level, prevention by construction of network level, prevention by client level. we designed a prevention system for Spam-mail and implemented it by Visual Basic.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.