# Optimizing Internet of Things Honeypots with Machine Learning: A Review  
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escalation, and web injection [2]. The reliance on real-time wireless communication further expands the attack surface. Honeypots play a vital role in IoT security by simulating attacks and capturing malicious activities. As the survey provided by [PERSON] [4] highlights, honeypots are used against various attacks, including malware, phishing, and DoS attacks. However, existing systems primarily focus on individual protocols and lack comprehensive platform simulations. Effective honeypots must exhibit realistic behaviors and interactions to deceive attackers. The diversity of IoT communication protocols complicates the development of versatile honeypots, and many protocols remain insufficiently tested [5]. Machine learning has emerged as a promising solution to address the challenges associated with IoT honeypots. It enables rapid analysis of extensive datasets, identifying attack patterns and malicious behaviors. Machine learning can automate security system adaptations, providing effective solutions for resource-constrained IoT environments [6]. [PERSON] [6] collected different machine learning techniques for various IoT threats, such as DoS and malware attacks. By leveraging historical attack data, machine learning models enhance threat detection and identify emerging attack vectors. Additionally, machine learning-driven honeypots can dynamically generate and adapt simulated environments, improving their realism and effectiveness [7]. This paper highlights the key characteristics of IoT honeypots with the application of machine learning techniques. In particular, the authors provide an in-depth analysis of specific honeypot and machine learning techniques, their functionalities, and benefits. The authors also introduce architectural approaches for IoT honeypots and compare findings to derive insights to optimize honeypot architectures and apply machine learning techniques in IoT security. The following research questions guide the analysis of this research work:  
RQ 1: What is the current state of research on the integration of machine learning in IoT honeypots? This question aims to provide an overview of existing studies and frameworks that explore the application of machine learning techniques in IoT honeypots. By reviewing the current literature, this research work seeks to identify key trends, methodologies, and research gaps, forming the basis for understanding how machine learning is being integrated into honeypot technologies. RQ 2: Are there existing IoT honeypots that utilize machine learning, and what techniques do they employ? The second research question examines practical implementations of machine learning-based IoT honeypots. It focuses on the specific machine learning techniques employed, such as supervised, unsupervised, or reinforcement learning, and their contributions to improving attack detection, classification, and response mechanisms. This analysis aims to highlight the practical benefits of machine learning in enhancing honeypot interactions, adaptability, and threat identification. RQ 3: What challenges can be addressed using machine learning, and what potential improvements can it bring to IoT honeypots? The third research question explores the broader implications of machine learning integration in IoT honeypots. By assessing the limitations and challenges currently faced by honeypots, this research work investigates how machine learning technologies can mitigate these issues. Additionally, this question examines the potential advantages of machine learning, including the development of dynamic honeypots and the combined use of multiple machine learning methodologies to create more adaptive and resilient security solutions.