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欺诈信息构成的垃圾农场往往导致了搜索引擎排名失效、客户搜索效率低下等问题。针对这种互联网作弊行为,提出了基于信息热度功率密度的垃圾农场发现系统,给出了该系统的结构模型和运行流程。该系统通过对网站的信息热功率密度分析,提取网页内容的访问热度等信息,应对垃圾农场的高密度反向搜索和热点采集,进行垃圾农场的识别。仿真实验证明,该系统对垃圾农场具有较好的发现能力和效率,误报率较低,系统资源开销较少。
Garbage farms that fraudulent information often results in issues such as invalid search engine rankings and inefficient customer searches. Aimed at this kind of Internet cheating behavior, a garbage farm discovery system based on information heat power density was proposed, and the structural model and operation flow of the system were given. The system analyzes the thermal power density of the website and extracts the information such as the popularity of the webpage content, and carries out high-density reverse search and hot spot collection of the garbage farm to identify the garbage farm. Simulation results show that the system has good ability of discovery and efficiency of garbage farms, lower false alarm rate and less system resource overhead.