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practical detection of click spams using efficient classification-based algorithms
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نویسنده
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fallah mahdieh ,zarifzadeh sajjad
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منبع
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international journal of information and communication technology research - 2018 - دوره : 10 - شماره : 2 - صفحه:63 -71
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چکیده
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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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کلیدواژه
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bot ,click spam ,user session modeling ,classification
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آدرس
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yazd university, department. of computer engineering, iran, yazd university, department of computer engineering, iran
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پست الکترونیکی
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szarifzadeh@yazd.ac.ir
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Authors
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