Volume 10, Issue 2 (6-2018)                   IJICTR 2018, 10(2): 63-71 | Back to browse issues page

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Fallah M, Zarifzadeh S. Practical Detection of Click Spams Using Efficient Classification-Based Algorithms. IJICTR. 2018; 10 (2) :63-71
URL: http://ijict.itrc.ac.ir/article-1-330-en.html
1- Department. of Computer Engineering Yazd University Yazd, Iran
2- Department of Computer Engineering Yazd University Yazd, Iran , szarifzadeh@yazd.ac.ir
Abstract:   (261 Views)
Most of today’s Internet services utilize user feedback (e.g. clicks) to improve the quality of their services. For example, search engines use click information as a key factor in document ranking. As a result, some websites cheat to get a higher rank by fraudulently absorbing clicks to their pages. This phenomenon, known as “Click Spam”, is initiated by programs called “Click Bot”. The problem of distinguishing bot-generated traffic from the user traffic is critical for the viability of Internet services, like search engines. In this paper, we propose a novel classification-based system to effectively identify fraudulent clicks in a practical manner. We first model user sessions with three different levels of features, i.e. session-based, user-based and IP-based features. Then, we classify sessions with two different methods: a one-class and a two-class classification that both work based on the well-known K-Nearest Neighbor algorithm. Finally, we analyze our methods with the real log of a Persian search engine. Experimental results show that the proposed algorithms can detect fraudulent clicks with a precision of up to 96% which outperform the previous works by more than 5%.
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Type of Study: Research | Subject: IT

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