With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks facing them have become more and more extensive. The frequent occurrence of network security incidents has caused huge losses. Facing an increasingly severe situation, it is necessary to adopt various network security technologies to solve the problem. Intrusion detection technology can detect internal and external network attacks, respond before the intrusion occurs, and send out alarm information for timely and effective processing. This article mainly introduces the research of cloud computing intrusion detection technology based on BP neural network (BP-NN), and intends to provide ideas and directions for the development of cloud computing intrusion detection technology based on BP-NN. This paper proposes research methods of cloud computing intrusion detection technology based on BP-NN, including BP-NN algorithm, neural network cloud computing intrusion detection technology and artificial bee colony optimization algorithm, which are used to conduct cloud computing intrusion detection technology experiment based on BP-NN; Proposed an artificial bee colony optimization neural network algorithm; designed a cloud computing intrusion detection system based on BP-NN. Experimental result shows that the average detection rate of the ABC-BP network algorithm is 92.67 %, which can effectively distinguish normal data from abnormal data.

随着网络技术的不断发展和网络规模的不断扩大，网络安全受到的威胁越来越多，面临的攻击也越来越广泛。网络安全事件频发，造成了巨大损失。面对日益严峻的形势，需要采用各种网络安全技术来解决问题。入侵检测技术可以检测内外网攻击，在入侵发生前作出反应，并发出告警信息以便及时有效处理。本文主要介绍了基于BP神经网络（BP-NN）的云计算入侵检测技术的研究，并拟为基于BP-NN的云计算入侵检测技术的发展提供思路和方向。本文提出了基于BP-NN的云计算入侵检测技术研究方法，包括BP-NN算法、神经网络云计算入侵检测技术和人工蜂群优化算法，用于基于BP-NN的云计算入侵检测技术实验。 BP神经网络；提出了一种人工蜂群优化神经网络算法；设计了一种基于BP-NN的云计算入侵检测系统。实验结果表明，ABC-BP网络算法的平均检测率为92.67%，可以有效区分正常数据和异常数据。本文提出了基于BP-NN的云计算入侵检测技术研究方法，包括BP-NN算法、神经网络云计算入侵检测技术和人工蜂群优化算法，用于基于BP-NN的云计算入侵检测技术实验。 BP神经网络；提出了一种人工蜂群优化神经网络算法；设计了一种基于BP-NN的云计算入侵检测系统。实验结果表明，ABC-BP网络算法的平均检测率为92.67%，可以有效区分正常数据和异常数据。本文提出了基于BP-NN的云计算入侵检测技术研究方法，包括BP-NN算法、神经网络云计算入侵检测技术和人工蜂群优化算法，用于基于BP-NN的云计算入侵检测技术实验。 BP神经网络；提出了一种人工蜂群优化神经网络算法；设计了一种基于BP-NN的云计算入侵检测系统。实验结果表明，ABC-BP网络算法的平均检测率为92.67%，可以有效区分正常数据和异常数据。用于基于BP-NN的云计算入侵检测技术实验；提出了一种人工蜂群优化神经网络算法；设计了一种基于BP-NN的云计算入侵检测系统。实验结果表明，ABC-BP网络算法的平均检测率为92.67%，可以有效区分正常数据和异常数据。用于基于BP-NN的云计算入侵检测技术实验；提出了一种人工蜂群优化神经网络算法；设计了一种基于BP-NN的云计算入侵检测系统。实验结果表明，ABC-BP网络算法的平均检测率为92.67%，可以有效区分正常数据和异常数据。