# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 7 Conclusions  
The increasing use of IoT devices has led to growing security concerns, necessitating advanced solutions to address emerging threats. Honeypots enhance IoT security by attracting and analyzing attackers. However, traditional honeypots struggle with adaptability and efficiency. This paper examines how machine learning enhances honeypot capabilities by improving threat detection and response mechanisms. A systematic literature review using the snowballing method explores the application of supervised, unsupervised, and reinforcement learning in optimizing honeypot architectures. This paper focuses on dynamic honeypots, which evolve to mislead attackers, and adaptive honeypots, which respond to threats in real time. By evaluating low-interaction, high-interaction, and hybrid honeypots, we determine how different machine learning techniques enhance detection and resource efficiency. The findings of this research work indicate that integrating multiple machine learning techniques enhances honeypot effectiveness (RQ1). Supervised, unsupervised, and reinforcement learning contribute to improved detection, adaptability, and automated response mechanisms, making honeypots more resilient against cyber threats. The comparison of dynamic and adaptive honeypots highlights their respective strengths. Dynamic honeypots are effective in misleading attackers over time, while adaptive honeypots provide real-time threat responses. This paper also emphasizes the role of low-interaction, high-interaction, and hybrid honeypots in optimizing threat detection and security resource allocation. Despite their advantages, machine learning-based honeypots face challenges such as high computational costs and limited real-world deployment (RQ2, RQ3). Future research should focus on developing lightweight machine learning models suitable for resource-constrained IoT environments. Long-term real-world testing, integration with emerging technologies like 5G and edge computing, and optimization techniques for model efficiency could further enhance the practicality and scalability of these systems. In conclusion, this paper provides valuable insights into the role of machine learning in improving IoT honeypots. Addressing existing challenges and exploring future research directions can lead to more intelligent, adaptable, and effective security solutions for IoT networks. Conceptualization, S.L., S.L.,-R.P., P.S., H.W., M.P., G.C. and N.D.; methodology, S.L., S.L.-R.P., P.S., H.W., M.P., G.C. and N.D.; validation, S.L., S.L.-R.P., P.S., H.W., M.P., G.C. and N.D.; investigation, S.L., S.L.-R.P., P.S. and H.W. ; resources, S.L., S.L.-R.P., P.S. and H.W. ; data curation, S.L., S.L.-R.P., P.S. and H.W. ; writing--original draft preparation, S.L., S.L.-R.P., P.S., H.W., M.P., G.C. and N.D.; writing--review and editing, M.P., G.C. and N.D.; visualization, S.L., S.L.-R.P., P.S., H.W., M.P., G.C. and N.D.; supervision, M.P., G.C. and N.D.; project administration, M.P., G.C. and N.D.; funding acquisition, N.D. All authors have read and agreed to the published version of the manuscript. * [PERSON] (2020) [PERSON] Research of MQTT, CoAP, HTTP and XMPP IoT Communication Protocols for Embedded Systems. In Proceedings of the 2020 XXIX International Scientific Conference Electronics (ET), Sozopol, Bulgaria, 16-18 September 2020; pp. 1-4. [CrossRef]
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