计算机与现代化 ›› 2024, Vol. 0 ›› Issue (04): 21-26.doi: 10.3969/j.issn.1006-2475.2024.04.004

• 算法设计与分析 • 上一篇    下一篇

基于多智能体遗传算法的云平台抗虚假数据注入攻击方法 #br# #br#

  


  1. (黑龙江省气象数据中心,黑龙江 哈尔滨 150001)
  • 出版日期:2024-04-30 发布日期:2024-05-13
  • 作者简介:王东岳(1974—),男,黑龙江哈尔滨人,高级工程师,本科,研究方向:气象信息网络系统,E-mail: lanlaichuif2n@163.com; 通信作者:刘浩(1981—),男,黑龙江哈尔滨人,高级工程师,硕士,研究方向:气象信息网络系统,大数据,E-mail: maomaothy@163.com。
  • 基金资助:
    教育部工程科技人才培养专项研究基金资助项目(19JDGC005)

Multi-agent Genetic Algorithm Based Cloud Platform Anti-fake Data Injection Attack Method


  1. (Heilongjiang Meteorology Data Center, Harbin 150001, China)

  • Online:2024-04-30 Published:2024-05-13

摘要: 摘要:为确保云平台内数据传输安全,提出一种基于多智能体遗传算法的云平台抗虚假数据注入攻击方法。采用开源平台OpenStack搭建云平台,并分析云平台虚假数据注入攻击过程;以该攻击过程为基础,结合Copula函数与GAN生成对抗网络构建虚假数据注入攻击检测框架,利用Copula GAN函数模型中的判别器与生成器对云平台原始量测数据进行对抗训练,再采用极端随机树分类器检测虚假数据,判断云平台中是否存在虚假数据注入攻击情况;利用三层攻防博弈模型防御云平台中的虚假数据注入攻击,同时由该模型为各条数据传输线路分配防御资源,并设置对应的约束条件;采用多智能体遗传算法对模型进行优化求解,完成云平台虚假数据注入攻击目标防御。实验结果表明,该方法可以精准检测云平台虚假数据并及时采取防御措施,具备较强的抗虚假数据注入攻击能力。

关键词: 关键词:多智能体, 遗传算法, 云平台, 虚假数据, 注入攻击, 攻击防御

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

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