网络中高速目标信息优化检测仿真研究

Simulation Research on Optimization of High Speed Target Information in Network

对网络中高速目标信息进行优化检测,可提高网络信息的检索速度。进行高速目标信息检测时,应建立在找出全部符合支持度的高速目标信息的频繁集基础上进行,但是网络运行时存在非线性和随机性,导致传统方法在非线性和随机性的干扰下无法准确获取高速目标信息的频繁集,不能对网络中高速目标信息进行有效检测。提出一种基于关联规则的网络中高速目标信息优化检测方法,对网络中的数据进行预处理,并引入了关联规则理论,找出全部符合支持度的高速目标信息的频繁集,依据频繁集获取关联规则,实现网络中高速目标信息优化检测。仿真结果表明,所提模型不仅扩展性高,而且在检测更多网络中高速目标信息的同时,能够保持较高的检测精度。

It can improve the speed of network information retrieval to make optimization detection for the high-speed target information in the network. The information detection should proceed based on finding out all frequent item set of high-speed target information corresponding to the support degree. However, there is nonlinearity and randomness during the network running. It makes traditional method cannot obtain the frequent item set of high-speed target information accurately under the interference of nonlinearity and randomness. Therefore, it cannot detect the high-speed target information effectively in the network. In this paper, we propose an optimization detection method of high-speed information in the network based on the association rules. Firstly, the method makes pre-treatment to the data in the network. Then, it introduces the association rules to find out all the frequent item set of high-speed target information corresponding to the support degree. Finally, it obtains the association rules according to the frequent item set and achieves the optimization detection of high-speed target information in the network. The simulation results show that the model mentioned above not only has high expansibility, but also can maintain a high detection accuracy in the meantime of detecting more high-speed target information.