基于隐马尔科夫模型的网络爬虫检测算法仿真

在网站的建设与维护中，为了提升服务器效率，加强安全保密性等原因需要区分普通用户和网络爬虫程序。但是一些不完善或恶意的设计使得针对爬虫程序的检测变得困难，这些爬虫程序不仅加重网站的负担，也危害了网络的安全。为了解决这一问题，本文提出一种利用行为模式进行检测的技术，采用隐马尔科夫模型描述行为模式，并使用Matlab仿真实现高精度的检测效果。结果表明，利用隐马尔科夫模型的检测技术可以实现高精确度和低错误率的网络爬虫检测。

Simulation of Web Crawler Detection Algorithm Based on Hidden Markov Model

In the construction and maintenance process of the website, in order to improve server efficiency, strengthen security and confidentiality, developers need to distinguish between human users and Web crawlers. However, some inappropriate or malicious designs make it difficult to detect crawlers. These crawlers not only increase the burden on the site, but also endanger the security of network. In order to solve the problem that it is difficult to detect crawlers, a detection algorithm based on behavior pattern is proposed, which uses hidden Markov model to describe the behavior patterns of different clients and uses Matlab simulation to achieve a highly accurate detection result. The simulation results show that the detection technology of hidden Markov model can detect Web crawler with high accuracy and low error rate.