Sosyal Ağlar için Hibrit Bir Spam Algılama Framework

Year 2023, Volume: 26 Issue: 2, 823 - 837, 05.07.2023

Oğuzhan Çıtlak , Murat Dörterler , İbrahim Dogru

https://doi.org/10.2339/politeknik.933785

Cited By: 3

Abstract

Sosyal ağların yaygınlaşması bu platformların kötü niyetli kişilerin hedefi haline gelmesine neden olmaktadır. Sosyal ağların kendi spam tespit sistemleri olmasına rağmen, bu sistemler bazen sosyal ağlarındaki spamları engelleyememektedir. Spam içerikler ve mesajlar, sosyal ağ kullanıcılarının güvenliğini ve performansını tehdit etmektedir. Bu çalışmada, üç bileşene dayalı bir spam hesap tespit modeli önerilmektedir. Kısa bağlantı analizi, makine öğrenmesi ve metin analizi önerilen modelde birlikte kullanılan bileşenlerdir. Bu amaçla, öncelikle bir veri seti oluşturulmuştur ve spam hesapların özellikleri belirlenmiştir. Sonra, bu veri setindeki mesajlarda yer alan hyperlinkler link analizi bileşeni ile analiz edilmektedir. Makine öğrenimi bileşeni, önceden belirlenen özniteliklere göre modellenmektedir. Ayrıca, sosyal ağ kullanıcılarının mesajları metin analizi yöntemi ilede analiz edilmektedir. Önerilen modelin web tabanlı bir uygulaması hayata geçirilmektedir. Önerilen model sayesinde yapılan deneysel çalışmalar sonucunda, önerilen modelin %95.69 oranında doğru performans gösterdiği tespit edilmektedir. Bu makalenin başarısının hesağlanmasında, hassas içerik oranının etkisi ile F puanı ve kesinlik değerlendirme metriklerine göre hesaplanmaktadır. Bu çalışmada, sosyal ağlardaki spam hesapların tespit edilmesi ve bu ağların spam tespit politikasının desteklenmesi amaçlanmaktadır.

Keywords

Sosyal ağlar, Kısa bağlantı analizi, Makine öğrenimi, Metin analizi, Spam tespiti

A Hybrid Spam Detection Framework for Social Networks

Abstract

The widespread use of social networks has caused these platforms to become the target of malicious people. Although social networks have their own spam detection systems, these systems sometimes may not prevent spams in their social networks. Spam contents and messages threaten the security and performance of users of these networks. A spam account detection framework based on three components is proposed in this study. Short link analysis, machine learning and text analysis are the components used together in the proposed framework. First, a dataset was created for this purpose and the attributes of spam accounts were determined. Later, the hyperlinks in the messages in this dataset were analyzed through link analysis component. The machine learning component was modelled through attributes. Moreover, the messages of the social network users were analyzed through text analysis method. A web-based application of the proposed model was put into practice. As a result of the experimental studies carried out thanks to the framework, it was determined that the proposed framework showed a performance of 95.69 %. The success of this article was calculated according to the F-measure and precision evaluation metrics under the influence of sensitive content rate. It is aimed to detect spam accounts on social network and the spam detection policy of these networks is intended to support.

Keywords

Social networks, Spam detection, Short link analysis, Machine learning, Text analysis

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