# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 6 Discussion
### Limitations  
While this paper provides valuable insights into the integration of machine learning into IoT honeypots, it aims to establish a foundation and represent the current state of research rather than solve a specific problem. Certain limitations associated with the scope and methodology of this research must be considered to provide context and adequately evaluate the findings. The systematic literature review was conducted within a limited three-month period. Although the time-frame imposed natural constraints, rigorous efforts were made to ensure the review is complete and reproducible. The relatively short time-frame influenced the focus on meaningful and representative studies to enable clear and coherent analysis. IoT security, machine learning, and honeypots are dynamic fields that are rapidly evolving. The findings of this paper reflect the state of research as of October 2024. The rapid development of new machine learning models, IoT technologies, and emerging threats means that some findings presented here may need to be reevaluated in the future. Additionally, it is worth mentioning that the literature the authors of this study reviewed predominantly focuses on short-term experiments or simulations. These studies lacked long-term deployment scenarios, which limited our ability to draw conclusions about the sustainable performance and adaptability of machine learning techniques in IoT honeypots. Furthermore, machine learning solutions with significant computational requirements were often recommended in the literature. These solutions may not be compatible with the limitations of resource-constrained IoT devices, such as limited processing power and storage capacity, which poses challenges for practical use that should be considered. Additionally, ensuring the robustness of machine learning models against adversarial attacks remains a crucial challenge. Many existing studies do not address how well these models can withstand sophisticated evasion techniques used by attackers, highlighting a critical research gap. Another significant limitation lies in data availability and quality. The effectiveness of machine learning-driven honeypots heavily depends on high-quality datasets for training and evaluation. However, obtaining real-world attack data is challenging due to privacy concerns, ethical considerations, and the constantly evolving nature of cyber threats. The reliance on publicly available datasets or simulated attack scenarios may not fully capture the complexity of real-world adversarial behaviors. Additionally, biases in dataset collection and annotation can influence model performance, leading to potential misclassifications and reduced generalizability of findings. Furthermore, the interpretability and transparency of machine learning models in IoT honeypots remain an ongoing challenge. Many state-of-the-art models function as \"black boxes\", making it difficult to explain their decision-making processes. This lack of interpretability can hinder trust and adoption, particularly in critical IoT infrastructures where explainability is crucial for security operations. These limitations provide the context for interpreting the results of this paper. Addressing them in future research will be essential to further enhancing the effectiveness, efficiency, and applicability of machine learning-based IoT honeypots in real-world environments. Finally, it is important to highlight that the real-world deployment of machine learning classifiers in IoT honeypot systems presents several implications. Firstly, the scalability and performance of these classifiers must be rigorously tested to handle large volumes of data and diverse attack patterns [73]. Ensuring robust performance in production environments requires thorough validation and continuous monitoring to adapt to evolving threats [74]. Additionally, the integration of machine learning models into existing security frameworks necessitates careful consideration of compatibility and interoperability [75].