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.

Phishing Detection System Using Extreme Learning Machines with Different Activation Function based on Majority Voting

“…This situation can cause problems such as more human effort and not detecting phishing URLs in a timely manner [4]. To tackle these disadvantages of phishing URL tanks, researchers primarily focused on traditional machine learning methodologies that can provide a more intelligent phishing detection [5][6][7][8][9][10][11][12]. In the traditional machine learning approach, feature selection is made with the help of cyber security experts, and then phishing detection is performed by using traditional machine learning algorithms.…”

Bilgisayarla İşlenen Suçlara Hızlı Müdahale, İnceleme, Analiz ve Raporlama Süreçlerinin Yeni Nesil Adli Bilişim Yöntemleri İle Etkin Yönetimi

“…Digital forensic information to be used as evidence in a forensic case is obtained from different technological equipments and various computer applications. Computer applications such as software, databases, web and e-mails are sources from which can be accessed digital forensic information [7], [8]. In addition to these sources, because the computer is allowed to transmit and share the necessary information, research that reveals network information can contribute significantly to the acquisition of needed forensic information [9].…”