云计算架构中恶意代码入侵自动监测系统设计

为了解决云计算架构中恶意代码以各种形式入侵产生损害,不能及时发现、维护而造成云计算架构安全性能降低,无法正常使用的问题,建立一套基于BP神经网络的入侵监测系统,实现对云计算架构中恶意代码入侵的自动监测,对及时监测入侵恶意代码及有效增加云计算架构安全有这直接而又重要作用；系统以STM32F103ZET6为主控芯片构建MUC主控单元,并通过EZ-USB FX2 USB2.0控制芯片将各个模块与其相连；采用LM2575系列的稳压器,为系统提供电源；软件设计过程中,采用BP神经网络法计算各恶意代码入侵的输出值,降低监测误差；通过实验测试表明,该系统可实现云计算架构中入侵恶意代码的自动监测功能,且具有扩展性强、操作方便等特点,对云计算架构的使用安全性具有重要的应用价值。

Design of malicious code intrusion automatic monitoring system in cloud computing architecture

In order to solve the malicious code of cloud computing architecture in various forms of intrusion damage, can not be found in time and maintenance caused by cloud computing architecture safety performance is reduced, not the normal use of the problem, based on a BP neural network intrusion detection system, realize the calculation of automatic monitoring in the framework of the invasion of the malicious code on the cloud, have an important role in the direct and timely monitoring of security computing architecture intrusion malicious code and effectively increase the cloud system; using STM32F103ZET6 as main control chip of the main control unit of MUC, and the EZ-USB FX2 USB2.0 control chip is connected with each module; using LM2575 series voltage regulator, power supply system; software design process, the output of the calculation of the invasion of the malicious code the value of using BP neural network method, reduce the monitoring error; the experiments show that the system can achieve the cloud The automatic monitoring function of intrusion malicious code in the architecture has the characteristics of strong expansibility and easy operation, and has important application value to the security of the cloud computing architecture.