• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.526-535
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• 2018

Many steganography algorithms have been studied, and steganalysis for detecting stego images which steganography is applied to has also been studied in parallel. Especially, in the case of the image steganalysis, the features such as ALE, SPAM, and SRMQ are extracted from the statistical characteristics of the image, and stego images are classified by learning the classifier using various machine learning algorithms. However, these studies did not consider the effect of image size, aspect ratio, or message-embedding rate, and thus the features might not function normally for images with conditions different from those used in the their studies. In this paper, we analyze the classification rate of the SPAM-based image stegnalysis against variety image sizes aspect ratios and message-embedding rates and verify its versatility.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.604-625
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• 2018

Nowadays, SMS spam has been overflowing in many countries. In fact, the standards of filtering SMS spam are different from country to country. However, the current technologies and researches about SMS spam filtering all focus on dividing SMS message into two classes: legitimate and illegitimate. It does not conform to the actual situation and need. Furthermore, they are facing several difficulties, such as: (1) High quality and large-scale SMS spam corpus is very scarce, fine categorized SMS spam corpus is even none at all. This seriously handicaps the researchers' studies. (2) The limited length of SMS messages lead to lack of enough features. These factors seriously degrade the performance of the traditional classifiers (such as SVM, K-NN, and Bayes). In this paper, we present a new fine categorized SMS spam corpus which is unique and the largest one as far as we know. In addition, we propose a classifier, which is based on the probability topic model. The classifier can alleviate feature sparse problem in the task of SMS spam filtering. Moreover, we compare the approach with three typical classifiers on the new SMS spam corpus. The experimental results show that the proposed approach is more effective for the task of SMS spam filtering.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2301-2308
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• 2013

These days, Rehabilitation psychology analysis system is being used by world wide web in everyday's life. And on the other hand, we are facing spam messages' problems. To block these spam message, we are using filtering or pricing systems. But these solutions are raising other problems such as impediment in reception or availability caused by false positive or payment resistance. To solve these problems, we propose an Indirect Model on Message Control System(IMMCS) which controls an unsolicited message and prevents an useful message from discarding. We design and implement the IMMCS to enhance the usefulness and the availability. Being tested the IMMCS to verify the usability and the efficiency, it gave us a very successful result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8B
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• pp.521-528
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• 2007

This paper proposes a message authentication scheme to resist potential spam threats in SIP-based VoIP services. Our scheme applies the extended HTTP digest authentication mechanism between the inbound proxy and the UAS to verify that a service request is coming through the valid inbound proxy. The proposed scheme is simple and requires minimal modification the current SIP standards, and effective to filter invalid peer-to-peer spam calls. In this paper, an experimental spam attack using modified open source was tested on a commercial VoIP networks to exploit the possibility of spam attacks in real environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.645-653
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• 2014

The purpose of this study is to provide low-cost and highly effective methods for customers to pick out SMS(Short Message Service) messages sent by financial institutions among SPAM messages and Smishing, which have rapidly spread recently and have caused critical issues. Above all, the study aims to list problems and limitations of the past efforts and measures to block SPAM messages and provide one method to overcome those limitations. Also, the study aims to verify the effectiveness of the method by implementation of them and conducting surveys of a broad range of customers.

• Journal of KIISE
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• v.42 no.6
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• pp.801-806
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• 2015

The Short Message Service (SMS) is one of the most popular communication tools in the world. As the cost of SMS decreases, SMS spam has been growing largely. Even though there are many existing studies on SMS spam detection, researchers commonly have limitation collecting users' private SMS contents. They need to gather the information related to social network as well as personal SMS due to the intelligent spammers being aware of the social networks. Therefore, this paper proposes the Social network Building Scheme for SMS spam detection (SBSS) algorithm that builds synthetic social network dataset realistically, without the collection of private information. Also, we analyze and categorize the attack types of SMS spam to build more complete and realistic social network dataset including SMS spam.

• 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.

• Journal of the Korea Society for Simulation
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• v.16 no.3
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• pp.75-87
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• 2007

As the VoIP service quality is getting better and many shortcomings are being overcome, users are getting interested in this service. Also, there are several additional features that provide a convenience to users such as presence service, instant messaging service and so on. But, as there are always two sides of rein, some security issues have users hesitate to make use of it. This paper deals with one of the issues, the VoIP spam problem. We took into account the signal pattern of voice message in spam call and we have constructed voice signal models of normal call, normal call with noise and spam call. Each voice signal case is inserted into our spam decision algorithm which detects the spam calls based on the amount of information in the call signal. We made use of the DEVS-$Java^{TM}$ for our modeling and simulation. The contribution of this work is in suggestion of a way to detect voice spam call signal and testing of the method using modeling and simulation methodology.