The increase of security incidents brings a challenge to the cloud computing security. Intrusion detection technologies have been applied to protect information in cloud from being compromised, and complicated learning‐based detection methods have been used to improve the performance of intrusion detection systems. Higher quality and well‐formed samples are crucial to the performance of detection algorithm. Therefore, we mainly study the intrusion detection model based on data optimization processing. In this article, we establish an intrusion detection algorithm based on ensemble support vector machine with bag representation. Specifically, the sample flows are divided into bags, where the sample flows in each bag are related to each other. Each bag contains multiple related data flows that can accurately reflect intrusion behavior, especially persistent intrusion. What's more, ensemble algorithm is applied to detection model, which greatly optimizes the performance of detection algorithm. The experimental results on open access datasets show that the proposed model detects the persistent attack with 90.58% recall.

中文翻译：

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云计算中基于袋表示的集成支持向量机入侵检测算法

安全事件的增多给云计算安全带来了挑战。入侵检测技术已被应用于保护云中的信息不被泄露，并已使用复杂的基于学习的检测方法来提高入侵检测系统的性能。更高质量和格式良好的样本对检测算法的性能至关重要。因此，我们主要研究基于数据优化处理的入侵检测模型。在本文中，我们建立了一种基于带袋表示的集成支持向量机的入侵检测算法。具体来说，将样本流分为袋子，每个袋子中的样本流是相互关联的。每个包包含多个相关数据流，可以准确反映入侵行为，尤其是持续入侵。更重要的是，在检测模型上应用了集成算法，极大地优化了检测算法的性能。在开放访问数据集上的实验结果表明，所提出的模型以 90.58% 的召回率检测到持久性攻击。