This paper presents a  
Several related survey papers on DDoS attacks and detection methods are available in the literature [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]. Most of these surveys concentrate on a particular portion of this subject. For example, the survey by [PERSON] and [PERSON] [32] focused on a statistical approach for DDoS attack detection. In [PERSON] et al. [29], research studies based on artificial intelligence and statistical methods were surveyed. In [28, 31, 33], only machine learning approaches for attack detection were emphasized. Table 1 presents a summary and comparison of our survey paper with other related surveys. In this table, a list of topics that were not covered, were partially covered, or were covered by other surveys is summarized. From Table 1, it is observed that related surveys either confine their analysis to certain DDoS attack detection methods [28, 29, 31, 32, 33] or only compare a small subset of them [24, 26, 27, 28, 33]. Recent articles on DDoS attack detection methods are not addressed in several other studies [23, 24, 25, 26, 27]. These, among other reasons, prompt the need to conduct an in-depth and updated survey on DDoS attack detection. Consequently, this paper presents a structured and broad survey of the existing research studies on DDoS attacks and detection methods in the IoT and other internet-enabled networks. A summary of the paper's main contributions is provided below:  
* A thorough description of DDoS attack categories and architecture was provided in this paper. Attack detection methods were classified, and research studies under each category are extensively discussed. The research studies in each category are then compared and analysed;
* Attack scenarios and detection studies in emerging networks such as IoDs, IoFT, FANET, RPL-based IoT, and NDN are also investigated;
* This paper covers Chi-square, Chao-based, and queueing model-based attack detection methods that were not covered in existing surveys;
* Apart from the DDoS attacks and detection methods, our survey also provides an overview of the benchmark dataset used for attack detection validation;
* Finally, several research issues and challenges associated with these methods are identified. A focus for future studies is also provided. The purpose of this study is to broaden the focus and provide an updated research direction on DDoS studies. Although this survey focused more on attack detection and studies in IoT, studies focusing on attack detection in SDN, RPL, NDN, and vehicular or flying things are also investigated and discussed. The remainder of the paper is structured as follows. Section 2 covers the taxonomy of DDoS attacks. It shows the attack architecture and the categories of attack. The categories of DDoS attack detection methods are illustrated in Section 3, along with a thorough review of the literature in each category. Section 4 discusses and compares some of the benchmark datasets used in DDoS attack detection research studies. In Section 5, a discussion of the key findings from the survey is presented. Section 6 provides a summary of some significant research challenges that could be the focus of future studies, and Section 7 concludes the paper. Figure 2: Analysis of the selected articles in the literature review: (**a**) temporal distribution of articles per year, (**b**) distribution of articles per source type and publisher.