The distributed and decentralized nature of cloud computing facilitates its adoption and expansion in different sectors of society such as education, government, information technology, business, and entertainment, etc. Cloud Computing provides a wide information technology landscape. Its existence in every section of society makes this computing paradigm prone to intrusions and attacks. A huge volume of data stored on cloud computing poses a high risk of security and privacy [6]. Therefore it is important to build a network intrusion detection system using an anomaly detection approach for a cloud computing network which can identify as many threats as possible with better assault identification level and less false positives. This paper discusses an effective network-based intrusion detection model utilizing an ensemble-based machine learning approach using four classifiers i.e., Boosted tree, bagged tree, subspace discriminant, and RUSBooted along with a voting scheme. The voting algorithm is incorporated into the framework to obtain a consolidated final prediction. Standard dataset and simulator namely, CICIDS 2017 and CloudSim were used for simulation and testing of the suggested model. The implementation results obtained by employing individual classifiers and combined result of all the four classifiers is compared along with comparing the proposed model with respect to existing Intrusion detection models. Results of implementation demonstrate the ability of the proposed model in the identification of intrusions in the cloud environment with a higher rate of detection and generation of minimal false alarm warnings, which suggests its dominance relative to state-of-the-art approaches. The implementation results show an accuracy of 97.24%.

云计算的分布式和分散性质促进了其在社会的不同领域（例如教育，政府，信息技术，商业和娱乐等）的采用和扩展。云计算提供了广泛的信息技术前景。它在社会各个阶层的存在使这种计算范例易于受到入侵和攻击。云计算上存储的大量数据带来了很高的安全性和隐私风险[6]。因此，重要的是使用针对云计算网络的异常检测方法来构建网络入侵检测系统，该系统可以以更好的攻击识别级别和更少的误报来识别尽可能多的威胁。本文讨论了一种有效的基于网络的入侵检测模型，该模型使用基于集成的机器学习方法，该方法使用四个分类器，即Boosted树，bagged树，子空间判别器和RUSBooted以及投票方案。投票算法被合并到框架中以获得合并的最终预测。标准数据集和模拟器（CICIDS 2017和CloudSim）用于建议模型的仿真和测试。将采用单个分类器和所有四个分类器的组合结果获得的实现结果进行比较，并与现有入侵检测模型进行比较。实施结果表明，该模型具有更高的检测率和最小的虚假警报生成率，能够识别云环境中的入侵，具有相对于最新方法的优势。实施结果表明准确性为97.24％。