Abstract: Abstract： In order to ensure the security of data transmission in cloud platforms， a multi-agent genetic algorithm based anti false data injection attack method for cloud platforms is proposed. We build a cloud platform using the open source platform OpenStack， and analyze the process of false data injection attacks on the cloud platform. Based on this attack process， a false data injection attack detection framework is constructed by combining Copula function and GAN generation countermeasures network. The discriminator and generator in the Copula GAN function model are used to conduct countermeasures training on the original measured data of the cloud platform， and then an extreme random tree classifier is used to detect false data to determine whether there is a false data injection attack in the cloud platform. Using a three-layer attack and defense game model to defend against false data injection attacks in the cloud platform， the model allocates defense resources for each data transmission line， and sets corresponding constraints. The model is optimized using a multi-agent genetic algorithm to complete the defense against false data injection attacks on cloud platforms. The experimental results show that this method can accurately detect false data on cloud platforms and take timely defensive measures， and has a strong ability to resist false data injection attacks.

Key words: Key words： multi-agent, genetic algorithm, cloud platform, false data, injection attack, attack defense