## 6 Conclusions  
In this paper, packet and flow-based network datasets derived from simulated IoT devices are analyzed with the objective of generating authentic datasets. The study involves capturing network packets in real-time using packet analyzer tools and network flow tracers from the Layer 2 addresses, tunnel data representation (MPLS, GRE, IPsec, etc. ), protocol identifications, SAPs, total hop count, L4 transport detection, RTP detection, and host flow applications aiding in the dataset development. The devices were then subjected to a botnet attack and were able to generate 6,000,000 flows of botnet attack and normal network data. The brute force ability of the tool leveraging techniques such as the HTTP stress toolkit was substantial in the process of obtaining and posting attacks to the HTTP requests in the network, while DNS and TCP scans and resolver stability analysis were utilized for TCP and UDP attacks through advanced probing and fingerprinting. Data preprocessing techniques, such as imblearn, normalization, correlation, and entropy selection, are utilized in the process of scaling the dataset. While binary classification was implemented on the generated dataset using three different machine learning models, the random forest classifier provided the optimal result of 99.9872% to classify the attack and normal network \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Year** & \
\begin{tabular}{l} **Comprehensive** \\ **IoT Simulation** \\ \end{tabular} &
\begin{tabular}{l} **Multi Attack** \\ **Scenarios** \\ \end{tabular} & **Telemetry Data** & **Label** & **IoT/IIoT** \\ \hline KDDCUP99/NSL-KDD [12] & 1998 & No & No & No & Yes & IoT \\ \hline UNSW NB15 [13] & 2015 & No & Yes & No & Yes & IoT \\ \hline AWID [18] & 2015 & Yes & Yes & No & Yes & IoT \\ \hline ISCX [14] & 2017 & No & Yes & No & Yes & IoT \\ \hline UNSW-IoT Trace [15] & 2018 & Yes & No & No & N/A & IoT \\ \hline BoT-IoT [17] & 2018 & Yes & Yes & No & Yes & IoT \\ \hline UNSW-IoT [16] & 2019 & Yes & Yes & No & Yes & IoT \\ \hline WUSTL-IIoT-2021 [19] & 2021 & Yes & Yes & No & Yes & IIoT \\ \hline Proposed Dataset & 2022 & Yes & Yes & Yes & Yes & IoT \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of widely utilized IoT datasets with proposed dataset. traffic. The boT-IoT dataset, which is one of the most popular datasets in the research field of IoT security, was implemented for cross-evaluation of the generated dataset. Nine different classes respective to the normal and attack network categories were evaluated for multiclass classification with the popular dataset. The implementation of nine different machine learning techniques on the dataset provided optimal results through the detailed analysis of datasets from preprocessing techniques. The ensemble approach was found to be particularly effective in achieving a high accuracy of 99.9936%. One of the key findings of this research is the successful implementation of a botnet attack subjected to a functional IoT network, enabling the generation of a dataset with both attack and normal network data. Additionally, it highlights the remarkable ability of machine learning models to distinguish between normal network traffic and attack traffic, considering the behavioral characteristics of the network as valuable features and parameters for developing appropriate machine learning models. This underscores the potential of employing machine learning applications, especially for classifying complex and imbalanced datasets, utilizing suitable data preprocessing techniques. The dataset possesses the potential to serve as a valuable resource for further investigations in the area of IoT security. Future endeavors could involve the utilization of cloud interfaces to facilitate interactions with the server, enabling the generation and storage of data on cloud networks. This approach is not limited to static data, but rather encompasses the dynamic generation of data over time. Conceptualization, A.K., R.B. and J.C.F. ; methodology, A.K., R.B. and J.C.F.