Practical Detection of Click Spams Using Classification-Based Algorithms

نویسندگان
Mahdieh Fallah
Sajjad Zarifzadeh
Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran
چکیده
 Most of today's internet services utilize user feedback (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". Thus, 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 classificationbased system to effectively identify fraudulent clicks in a practical manner. We first model user sessions as a set of features. Then, we classify user sessions with a one-class classification algorithm that works based on the wellknown K-Nearest Neighbor algorithm. Finally, we analyze our methods with the real log of a Persian search engine. Experimental results show that the proposed algorithm can detect fraudulent clicks with a precision of up to 96% which outperform the previous works by more than 5%. 

کلیدواژه‌ها

علوم رایانش و فناوری اطلاعات
دوره 14، شماره 1
بهار و تابستان
بهار 1395