• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2849-2865
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• 2012

In order to block mobile junk messages automatically, many studies on spam filters have applied machine learning algorithms. Most previous research focused only on the accuracy rate of spam filters from the view point of the algorithm used, not on individual user's preferences. In terms of individual taste, the spam filters implemented on a mobile device have the advantage over spam filters on a network node, because it deals with only incoming messages on the users' phone and generates no additional traffic during the filtering process. However, a spam filter on a mobile phone has to consider the consumption of resources, because energy, memory and computing ability are limited. Moreover, as time passes an increasing number of feature words are likely to exhaust mobile resources. In this paper we propose a spam filter model distributed between a users' computer and smart phone. We expect the model to follow personal decision boundaries and use the uniform resources of smart phones. An authorized user's computer takes on the more complex and time consuming jobs, such as feature selection and training, while the smart phone performs only the minimum amount of work for filtering and utilizes the results of the information calculated on the desktop. Our experiments show that the accuracy of our method is more than 95% with Na$\ddot{i}$ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.

• The KIPS Transactions:PartC
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• v.18C no.2
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• pp.61-70
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• 2011

Even though internet community is becoming the basic communication tool with rapidly changing internet environment, its damage is on the rise due to increasing spam messages of unspecified individuals. Currently, various methods to block spam mails, but studies on block spam messages from being automatically posted in community are still insufficient. This study suggested methods to block spam messages in internet community by collecting spam messages posted in internet community to analyze characteristics and frequencies of the messages and create block regulations. It collects spam messages of all messages saved in database of internet community, analyze the collected messages to create normalized rules that can define spam messages, and inspect spam messages among posted messages by using the regulations to block them. The suggested method has a structure that can block various types of spam messages by using information of spam messages collected and flexibly deal with changing spam message types.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.122-135
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• 2011

Mobile spam message is a harmful factor which makes receivers to be annoyed and leads to unnecessary social cost. Unwanted junk messages flowing to a smart phone ruin main purpose of the smart work system to enhance the productivity, so we need to study on this area. In this paper, we proposed a novel spam filter on the smartphone in order to reduce computing process and improve the accuracy rate by feedback of error results to a training sample set. As the spam classifier operates on the smartphone independently with training on only user's received data, it could reflect user preference. The authorized personal computer takes on heavy works, such as preprocessing, feature selecting and training process, and the smartphone takes on light works to block junk messages. Experimental results showed reasonable accuracy rate of over 95%, and we found that the application occupied constant computing resources while running on the phone.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.113-120
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• 2009

Currently the verbal abuse in text message over on-line game is so serious. However we do not have any effective policy or technical tools yet. Till now in order to cope with this problem, the online game service providers have accumulated a set of forbidden words and applied this list on the textual word used in on-line game, which is called 'Swear filter'. But young on-line game players easily avoid this filtering method by coining another words which is not kept in the list. Especially Korean is very easy to make new variations of a vulgar word. In this paper, we propose one smart filtering algorithm to identify newly coined profanities. Important features of our method include the canonical form transformation of coined profanities, semi-global alignment between in the level of consonant and vowel units. For experiment, we have collected more than 1000 newly coined vulgar words in on-line gaming sites and tested these word against our methods. where our system have successfully filtered more than 90% of those newly coined vulgar words.

• Journal of IKEEE
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• v.23 no.4
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• pp.1393-1399
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• 2019

With the rapid growth of the Internet, millions of indiscriminate advertising SMS are sent every day because of the convenience of sending and receiving data. Although we still use methods to block spam words manually, we have been actively researching how to filter spam in a various ways as machine learning emerged. However, spam words and patterns are constantly changing to avoid being filtered, so existing machine learning mechanisms cannot detect or adapt to new words and patterns. Recently, the concept of Lifelong Learning emerged to overcome these limitations, using existing knowledge to keep learning new knowledge continuously. In this paper, we propose a method of spam filtering system using ensemble techniques of naive bayesian which is most commonly used in document classification and LLML(Lifelong Machine Learning). We validate the performance of lifelong learning by applying the model ELLA and the Naive Bayes most commonly used in existing spam filters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.91-94
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• 2011

The LBSNS(Location-Based Social Networking Service) service has been well-received by researchers and end-users, such as Twitter. Location-Based service of Twitter is now structured that users could not subscribe the information of their interesting local area. Those who being following from someone tweet message included information of local area to them just for their own interesting. However, follower may receive that kind of tweet. In order to handle the problem, we propose filtering technique using spatial join. The first work for filtering technique is to add a location information to tweets and users. In this paper, location information is represented by MBR(Minimum Bounding Rectangle). Location information is divided into dynamic property and static property. Suppose that users are continuously moving, that means one of the dynamic property's example. At this time, a massive continous query could cause the problem in server. In this paper, we create Virtual Grid on Google Map for reducing frequency of query, and conclude that it is useful for server.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1092-1100
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• 2004

An E-mails have regarded as one of the most popular methods for exchanging information because of easy usage and low cost. Meanwhile, exponentially growing unwanted mails in user's mailbox have been raised as main problem. Recognizing this issue, Korean government established a law in order to prevent e-mail abuse. In this paper we suggest hybrid spam mail filtering system using weighted Bayesian classifier which is extended from naive Bayesian classifier by adding the concept of preprocessing and intelligent agents. This system can classify spam mails automatically by using training data without manual definition of message rules. Particularly, we improved filtering efficiency by imposing weight on some character by feature extraction from spam mails. Finally, we show efficiency comparison among four cases - naive Bayesian, weighting on e-mail header, weighting on HTML tags, weighting on hyperlinks and combining all of four cases. As compared with naive Bayesian classifier, the proposed system obtained 5.7% decreased precision, while the recall and F-measure of this system increased by 33.3% and 31.2%, respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.223-225
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• 2002

인터넷의 급속한 성장과 함께 E-Mail은 대표적인 통신수단의 하나가 되어버렸다. 편리하다는 점을 이용해서 엄청난 양의 스팸메일이 매일같이 쏟아져 오고 , 그 문제점의 심각성에 정보통신부에서 정보통신망 이용촉진 및 정보보호 등에 관한 법률이라는 새로운 법률까지 생겨났다. 본 논문에서는 이 법률에서 요구하는 '광고'라는 문구를 걸러내는 등의 메시지 규칙을 갖는 시스템과 기존의 문서 분류에 널리 쓰이던 나이브 베이지안 분류자(Naive Baesian Classifier)를 결합한 스팸 메일 필터링 시스템(Spam-mail Fitering System)을 제안한다. 제안된 시스템에서는 사용자가 직접 규칙을 작성할 필요없이 학습한 데이터를 갖고 자동으로 스팸메일을 분류할 수가 있다. 들어온 메일은 메시지 규칙 기반 필터가 먼저 적용되고, 메세지 규칙 기반 필터에서 분류되지 않으면 나이브 베이지안 필터에서 분류된다. 실험에서는 제안된 시스템의 성능을 평가하기 위해서 메시지 규칙을 사용한 시스템 및 나이브 베이지만 분류자 시스템과 비교 평가하였다. 또한 임계치를 변경함으로써 제안된 시스템의 성능을 높일 수있도록 하였다.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1065-1072
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• 2017

As smart devices and social network services (SNSs) become increasingly pervasive, individuals produce large amounts of data in real time. Accordingly, studies on unstructured data analysis are actively being conducted to solve the resultant problem of information overload and to facilitate effective data processing. Many such studies are conducted for filtering inappropriate information. In this paper, a feature-weighting method considering SNS-message features is proposed for the classification of short text messages generated on SNSs, using Markov logic networks for category inference. The performance of the proposed method is verified through a comparison with an existing frequency-based classification methods.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.199-208
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• 2004

In this paper, we propose and implement SIA System for filtering redundant alert messages and dividing them into four statuses. Also, we confirm that our system can find and analyze vulnerability types of network intrusion by attackers in a managed network, so that it provides very effective means for security managers to cope with security threats in real time.