• Journal of KIISE
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• v.41 no.10
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• pp.785-791
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• 2014

Twitter is one of the most famous SNS(Social Network Service) in the world. Twitter spammer accounts that are created easily by E-mail authentication deliver harmful content to twitter users. This paper presents a spammer detection method that utilizes features based on the relationship between users in twitter. Relationship-based features include friends relationship that represents user preferences and type relationship that represents similarity between users. We compared the performance of the proposed method and conventional spammer detection method on a dataset with 3% to 30% spammer ratio, and the experimental results show that proposed method outperformed conventional method in Naive Bayesian Classification and Decision Tree Learning.

• Journal of KIISE
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• v.44 no.11
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• pp.1231-1235
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• 2017

Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.185-186
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• 2007

ITSP(Internet Telephony Service Provider)를 이용한 VoIP 서비스의 사용자가 증가함에 따라 VoIP 스팸은 큰 문제로 대두되고 있다. 기존의 일반 전화 때부터 사용되던 스팸은 실시간적 음성 통신이라는 특성상 콘텐츠 필터링을 하기 어렵기 때문에 롤 행위 패턴 조사를 통해 스패머(Spammer)를 구분하고 있다. 그러나 잘못된 오판으로 인한 문제와 스팸으로 인식하는 임계값을 넘지 않는 한도의 스팸 전송, 그리고 여러 사용자가 하나의 번호를 공유하여 사용하는 경우에는 여전히 스팸의 위협이 남아 있다. 이에 본 논문에서는 튜링 테스트를 이용한 VoIP 스팸 방지를 제안한다. 제안된 방법은 송신자에게 튜링 테스트를 거치게 하고, 튜링 테스트를 통과한 사용자만 수신자와 연결이 되는 방식으로 동작한다. 또한 튜링 테스트를 통과한 정상적인 사용자에게는 티켓을 줌으로써 재발신시 거쳐야하는 튜링 테스트의 번거로움을 줄일 수 있다. 제안된 방법은 ASUS WL-500G 무선 공유기 및 Asterisk IP-PBX에서 구현되었고 실험을 통해 제안된 방법의 유효성을 검증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.343-346
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• 2002

스팸메일이 급증함에 따라 신뢰할 수 있는 전자메일 필터의 요구가 늘어나는 추세다. 스팸메일을 보내는 스패머(spammer)의 거의 대부분은 광고가 주요 목적이다. 멀티미디어(multimedia)기반의 전자메일은 정보전달 및 시각효과가 뛰어나 스패머가 선호하는 전자메일의 한 형태이다. 이런 종류의 전자메일은 텍스트 기반(基盤) 스팸메일 필터의 성능을 떨어뜨리거나 필터링을 아예 불가능하게 한다. 본 연구에서 발신지(發信地) 추적기법과 사례기반학습을 이용해 신뢰할 수 있는 한국어 스팸 메일필터를 설계 및 구현하였다.

• The KIPS Transactions:PartC
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• v.12C no.3 s.99
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• pp.361-368
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• 2005

Internet mail has become a common communication method around the world because of tremendous Internet service usage increment. In other respect, Most Internet users' mail addresses are exposed to spammer, and the damage of Junk mail is growing bigger and bigger. These days, Junk mail delivery problem is becoming more serious, because this is used for an attack or propagation scheme of malicious code. It's a most dangerous dominant cause for computer system accident. This paper shows the Junk mail filtering model and implementation which is based on FQDN (Fully Qualified Domain Name) check and evaluates it for proposing advanced scheme against Junk mail.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.10
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• pp.99-107
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• 2008

In this paper, we analyse the vulnerability of spam attacks and develop the Spamtester to confirm these spam attacks in SIP-based VoIP networks. Although there are several spam attacks on VoIP networks, the detail information for the SPIT is not enough to confirm the procedure and the result of spam attacks on VoIP networks. Specially, the spam attacks through abnormal process are difficult to trace the sender of spam. Also, it is not easy to impose the legal restriction to the spammer because of lack of information for the spam attack. Therefore, on VoIP networks, the possible scenario and detail procedure for VoIP spam is needed to be confirmed. This paper designes and implementes the spamtester, which is helpful to protect VoIP networks from the spam attacks.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.41-53
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• 2012

The technologies used by the malicious codes have been being advanced and complicated through a merge of the existing techniques, while the damages by the malicious codes are moving from individuals and industries to organizations and countries. In this situation, the security experts are corresponding with the static analysis and the dynamic analysis such as signature searching and reverse engineering, respectively. However, they have had a hard time to respond against the obfuscated intelligent new zero day malicious codes. Therefore, it is required to prepare a process for a preliminary investigation and consequent detailed investigation on the infection sequence and the hiding mechanism to neutralize the malicious code. In this paper, we studied the formalization of the process against the infection sequence and the file hiding techniques with an empirical application to the Rustock malicious code that is most notorious as a spammer. Using the result, it is expected to promptly respond to newly released malicious codes.