In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

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软件定义网络中基于机器学习的僵尸网络检测：系统综述

在最近的几十年中，互联网极大地发展和改变了世界，而这反过来又带来了许多网络攻击。网络安全是对社会最严重的威胁之一，每年花费数百万美元。最重要的问题仍然是：这些攻击从何而来？答案是僵尸网络为网络攻击提供了平台。对于许多组织而言，僵尸网络辅助的攻击是一种可怕的威胁，它可能造成财务损失，并使全球受害者受其打击。因此，必须保护组织免受僵尸网络辅助的攻击。软件定义网络（SDN）已成为对此最有希望的范例之一，因为它允许网络管理和配置的复杂性呈指数增长。在网络管理方面，SDN与传统方法相比具有显着优势，因为SDN将控制平面与网络设备分开。但是，安全挑战不断出现，尽管带来了很多好处，但仍需要使用不同类型的实施策略来传播攻击媒介。该调查的主要目的是通过使用系统的评论和荟萃分析（PRISMA）指南来评估僵尸网络检测技术。我们根据机器学习评估了自2006年以来在僵尸网络检测领域以及SDN领域自2015年以来发表的各种文章。具体来说，我们使用了影响因子最高的顶级期刊。在本文中，我们旨在详细阐述有关僵尸网络攻击，检测技术，机器学习和SDN的几个研究领域。