Cloud computing model provides on demand, elastic and fully managed computer system resources and services to organizations. However, attacks on cloud components can cause inestimable losses to cloud service providers and cloud users. One such category of attacks is the Distributed Denial of Service (DDoS), which can have serious consequences including impaired customer experience, service outage and in severe cases, complete shutdown and total economic unsustainability. Advances in Internet of Things (IoT) and network connectivity have inadvertently facilitated launch of DDoS attacks which have increased in volume, frequency and intensity. Recent DDoS attacks involving new attack vectors and strategies, have precipitated the need for this survey.

In this survey, we mainly focus on finding the gaps, as well as bridging those gaps between the future potential DDoS attacks and state-of-the-art scientific and commercial DDoS attack defending solutions. It seeks to highlight the need for a comprehensive detection approach by presenting the recent threat landscape and major cloud attack incidents, estimates of future DDoS, illustrative use cases, commercial DDoS solutions, and the laws governing DDoS attacks in different nations. An up-to-date survey of DDoS detection methods, particularly anomaly based detection, available research tools, platforms and datasets, has been given. This paper further explores the use of machine learning methods for detection of DDoS attacks and investigates features, strengths, weaknesses, tools, datasets, and evaluates results of the methods in the context of the cloud. A summary comparison of statistical, machine learning and hybrid methods has been brought forth based on detailed analysis. This paper is intended to serve as a ready reference for the research community to develop effective and innovative detection mechanisms for forthcoming DDoS attacks in the cloud environment. It will also sensitize cloud users and providers to the urgent need to invest in deployment of DDoS detection mechanisms to secure their assets.

云计算模型为组织提供按需，弹性和完全托管的计算机系统资源和服务。但是，对云组件的攻击可能会给云服务提供商和云用户造成不可估量的损失。此类攻击之一就是分布式拒绝服务（DDoS），它可能会导致严重的后果，包括客户体验受损，服务中断，甚至在严重的情况下，彻底关闭并造成完全的经济不可持续性。物联网（IoT）和网络连接的进步无意中促进了DDoS攻击的发起，DDoS攻击的数量，频率和强度都在增加。最近涉及新的攻击媒介和策略的DDoS攻击催生了对此调查的需求。

在本次调查中，我们主要着眼于发现差距，并弥合未来潜在的DDoS攻击与最新的科学和商业DDoS攻击防御解决方案之间的差距。它试图通过提出最近的威胁状况和主要的云攻击事件，对未来DDoS的估计，示例性用例，商业DDoS解决方案以及在不同国家/地区管理DDoS攻击的法律，突出强调对综合检测方法的需求。已经给出了DDoS检测方法的最新调查，特别是基于异常的检测，可用的研究工具，平台和数据集。本文进一步探讨了使用机器学习方法检测DDoS攻击，并研究了功能，优势，劣势，工具，数据集，并在云环境中评估方法的结果。在详细分析的基础上，对统计，机器学习和混合方法进行了总结比较。本文旨在为研究社区开发有效和创新的检测机制，以应对即将在云环境中发生的DDoS攻击提供参考。它还将使云用户和提供商意识到迫切需要投资部署DDoS检测机制以保护其资产。