# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 4 Methodology
### Data Analysis  
For the analysis of the selected literature, this study adopted a systematic approach to extract and synthesize key insights from the literature in the dataset of this research work. The primary objective was to identify recurring themes, key methodologies, and trends in the use of machine learning for the development of IoT honeypots. To achieve this, this study employed [PERSON]'s concept-matrix approach [9], which is a widely used method for organizing and categorizing the findings from systematic literature reviews. The concept-matrix served as a tool for mapping out the different dimensions of the literature included in the review. This matrix allowed us to systematically compare and contrast various aspects of the research, identify gaps in the literature, and highlight areas where different literature converged or diverged in their findings. The concept-matrix was structured around four key dimensions that emerged from the literature: Interaction honeypots, machine learning techniques, detection architecture, and classifier technology. The specific classification of each paper to the four dimensions can be found in Table 3. Each of these dimensions corresponds to a critical aspect of IoT honeypot development in the context of machine learning. The interaction honeypot dimension relates to the level of engagement that the honeypot has with potential attackers, ranging from low interaction to high interaction. The dimension of dimension of machtechniques covers the various algorithms and techniques employed in analyzing and detecting malicious activities within these honeypots. The detection architecture refers to the systems and frameworks designed to detect and classify malicious behavior, while the classifier dimension focuses on the techniques used to categorize or identify attacker behavior. In general, the data analysis phase was instrumental in organizing the literature and generating a comprehensive understanding of the various approaches to using machine learning in the development and deployment of IoT honeypots. Through the systematic application of the concept-matrix and qualitative analysis techniques, we were able to extract meaningful insights that informed the conclusions and recommendations of this research work. In the following, we explain the findings in each dimension of the concept-matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Hits** & **Relevant Literature** & **Selected Literature** \\ \hline ACM & 40 & & \\ \hline IEEE Xplore & 219 & & \\ \hline Scopus & 246 & & \\ \hline DTU Find & 265 & & \\ \hline Backward & 1314 & 28 & 10 \\ \hline Forward & 674 & 53 & 5 \\ \hline Total & & & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of literature search results. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Author** & **Year** & \multicolumn{3}{c}{**Interaction** & **Homepots**} & \multicolumn{3}{c}{**Machine Learning Technique**} & \multicolumn{3}{c}{**Detection Architecture**} & \multicolumn{3}{c}{**Classifiers**} \\ \cline{3-10}  & **Low Interaction** & **High Interaction** & **Supervised** & **Unsupervised** & **Reinforcement** & **Malware Detection** & **Adaptive Homepots** & **Method** & **Framework** \\ \hline Absh