Practical Detection of Click Spams Using Efficient Classification-Based Algorithms

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%.