# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 3 Comparison of Existing Surveys  
In the existing literature, several surveys are already available that are thematically related to this research work. It is noticeable that these either focus on the use of machine learning techniques in specific cybersecurity domains or on honeypots in IoT environments, but without establishing a connection between the two aspects. In addition, most of the surveys are from 2018. This research work addresses this research gap by examining the application of machine learning techniques in the context of IoT honeypots. The thematic classification of the existing surveys is presented in Table 1.  
This categorization makes it clear that, so far, no comprehensive survey exists that combines IoT honeypots with machine learning techniques. The aim of this research work is therefore to address this research gap and systematically analyze existing solutions. The paper by [PERSON] et al. [18] belongs to the category of Machine Learning Techniques in Cybersecurity, as it extensively examines the use of machine learning and deep learning for securing 5G network slices. It analyzes the security risks that arise throughout the lifecycle of these virtual networks and illustrates how machine learning algorithms can be applied for attack detection, fault prevention, and network security optimization. Additionally, the paper provides a comprehensive overview of existing machine learning-based security solutions, compares various approaches, and develops a taxonomy that systematically describes the application of machine learning for different network functions. Since the focus is clearly on machine learning technologies for improving cybersecurity, this paper is categorized accordingly. The paper by [PERSON] et al. [19] falls into both the Honeypot Technology and IoT Environment categories, as it deals with the use of honeypots and honeynets to secure IoT, IIoT, and CPS systems. It analyzes the increasing threats to these interconnected environments due to their inherent vulnerabilities and demonstrates how honeypots can serve as a deception technology to identify attackers and better understand their methods. Furthermore, the paper presents a detailed taxonomy of existing honeypot and honeynet approaches, describes key design factors, and discusses open challenges in this research area. Due to its comprehensive approach, it is classified under both honeypot technology and IoT environments. The paper by [PERSON] et al. [20] belongs to the category Machine Learning Techniques in Cybersecurity, as it focuses on detecting and mitigating DDoS attacks in software-defined networks (SDNs) while analyzing various machine learning-based security solutions. It examines existing methods for identifying and mitigating DDoS attacks and evaluates them based on their effectiveness. Additionally, different types of DDoS attacks are described, along with the datasets, tools, and simulators used for attack detection. A key component \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Year** & **ML Techniques** & **Honeypot Technology** & **IoT Environment** \\ \hline [PERSON] [18] & 2022 & X & & \\ [PERSON] [19] & 2021 & & X & X \\ [PERSON] [20] & 2024 & X & & \\ [PERSON] [21] & 2018 & & X & partly \\ [PERSON] [22] & 2018 & & X & X \\ [PERSON] [23] & 2018 & & X & X \\ This paper & 2025 & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing surveys. of the paper is the comparative analysis of different DDoS detection techniques, where machine learning models play a crucial role. Since the paper strongly emphasizes machine learning-based security measures, it is clearly assigned to this category. The paper by [PERSON] et al. [21] is primarily categorized under Honeypot Technology, as it addresses various security threats to IoT devices and highlights honeypots as an effective protective measure against attacks.