Distributed denial of service (DDoS) attack is a serious security threat to cloud computing that affects the availability of cloud services. Therefore, defending against these attacks becomes imperative. In this paper, we present a DDoS attack detection system based on an improved Self-adaptive evolutionary extreme learning machine (SaE-ELM). SaE-ELM model is improved by incorporating two more features. Firstly, it can adapt the best suitable crossover operator. Secondly, it can automatically determine the appropriate number of hidden layer neurons. These features improve the learning and classification capabilities of the model. The proposed system is evaluated using four datasets namely, NSL-KDD, ISCX IDS 2012, UNSW-NB15, and CICIDS 2017. It achieves the detection accuracy of 86.80%, 98.90%, 89.17%, and 99.99% with NSL-KDD, ISCX IDS 2012, UNSW-NB15, and CICIDS 2017 datasets, respectively. The experiments show that the performance of the proposed attack detection system is better than the system based on original SaE-ELM and state-of-the-art techniques. However, it shows a longer training time than SaE-ELM based system.

分布式拒绝服务（DDoS）攻击是对云计算的严重安全威胁，会影响云服务的可用性。因此，防御这些攻击变得势在必行。在本文中，我们提出了一种基于改进的自适应进化极端学习机（SaE-ELM）的DDoS攻击检测系统。通过合并两个以上功能改进了SaE-ELM模型。首先，它可以适应最合适的交叉算子。其次，它可以自动确定隐藏层神经元的适当数量。这些功能改善了模型的学习和分类能力。该系统使用NSL-KDD，ISCX IDS 2012，UNSW-NB15和CICIDS 2017这四个数据集进行了评估。使用NSL-KDD，ISCX可以达到86.80％，98.90％，89.17％和99.99％的检测精度。 IDS 2012，分别是UNSW-NB15和CICIDS 2017数据集。实验表明，所提出的攻击检测系统的性能优于基于原始SaE-ELM和最新技术的系统。但是，与基于SaE-ELM的系统相比，它显示出更长的培训时间。