## 1 Introduction
Data traffic from IoT devices has been found to be distinct; it often exchanges data with fixed sets of endpoints rather than a vast diversity in penetration of its web servers, which helps to track the regular traffic and disobey the attack traffic according to the behavior and penetration change. IoT devices also have distinct states, so the behavior can be conveniently differentiated [6]. Traditional security methods that only react after an attack has been detected may not be sufficient to protect the network. To prevent these attacks from affecting the network's performance, a system that deploys smart and state-of-the-art security measures is necessary for IoT networks. Using proactive security methods can mitigate potential security weaknesses in the network. These security measures should also not cause any delays in the network, as IoT networks require low latency. To meet these requirements, AI and ML approaches can be used to provide the necessary intelligence to create smart security systems. AI and ML algorithms are able to analyze large amounts of data and identify common patterns that indicate an intrusion. Datasets are important for the effective use of intrusion-based security systems. Without a dataset that resembles the network flows in the IoT, it is difficult to validate the effectiveness of AI-based security measures. As IoT networks become increasingly important, having a dataset that can be used for this purpose will be a valuable contribution. By 2025, it is anticipated that the global market for IoT end-user solutions will reach approximately $1.6 trillion, posing a new growing danger from exposed telnet ports, out-of-date Linux firmware, and numerous other related vulnerabilities [7]. The expansion of these insecure IoT devices opens up wide varieties of new cybersecurity attack factors, most notably IoT Botnet attacks. IoT botnet attacks have been more frequent, with several cases of attacks on distributed networks, even on the most popular and security-concerned devices and networks, such as the likes of GitHub, Twitter, Reddit, Netflix, Airbnb, etc. [8]. In this study, an IoT architecture was implemented to create a network dataset. A botnet attack scenario was simulated on this architecture, which was designed to resemble a real-world environment. This enables the idea of recording and generating datasets that can be utilized to train machine learning models for the detection of normal and attack networks. The idea is to obtain a better understanding of the behavior of botnets in IoT networks for the development of more effective methods to detect and prevent them. The use of a realistic IoT architecture and simulated botnet attack enables the gathering of valuable data that can be used to enhance the security of IoT networks in the future. The major contributions of this study are listed as follows:  
1. A novel IoT device dataset was introduced, based on a smart health service testbed, that included both attack and normal network traffic. The dataset was preprocessed and feature-engineered using advanced machine-learning techniques, enabling the models to achieve higher accuracy;
2. The generated dataset was cross-evaluated with an existing IoT intrusion detection system dataset using various machine learning algorithms. The results demonstrated the competency of the generated dataset in improving the performance of intrusion detection systems and revealed unique features that make it a valuable addition to the field of IoT intrusion detection;
3. Nine popular machine learning model classifiers were applied to the dataset to classify attack and normal network traffic. The models utilized advanced approaches to detect anomalies and patterns in the network traffic, resulting in better classification accuracy;
4. The study demonstrated the competency of advanced machine learning techniques for detecting and classifying botnet attacks and showed that machine learning models could effectively differentiate between normal and attack traffic.