# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 6 Discussion
### Combined Advantages of Multiple Machine Learning Techniques in Internet of Things Honeypots  
In the field of IoT honeypots, the combination of multiple machine learning techniques is of great significance. Supervised learning is highly accurate in detecting known attack patterns. It can be trained with labeled data to recognize specific attack signatures and behaviors that have been previously identified. For example, it can effectively identify common malware infections or known types of network intrusions. Unsupervised learning, however, shines in discovering unknown abnormal patterns. It can analyze the data without prior knowledge of what is normal or abnormal and detect unusual behaviors that might indicate new or emerging threats. Reinforcement learning optimizes the system's defense strategy. It enables the honeypot to adapt and improve its responses over time based on the rewards or punishments it receives from the environment.  
The combination of these three methods offers several advantages. Firstly, in the initial stage, unsupervised learning helps to identify potential anomalies. It scans the IoT environment and flags any data points or behaviors that deviate from the norm. Then, supervised learning comes into play to classify these anomalies. It uses its learned models to determine whether the flagged items are actual attacks or just false positives. Finally, reinforcement learning takes the results from the previous two steps and optimizes the defense behavior of the honeypot. It decides on the best actions to take, such as adjusting the configuration of the honeypot or initiating countermeasures. This sequential process allows for a more comprehensive and accurate threat detection and response. Moreover, the combination of multiple methods enables more comprehensive threat detection and response. It covers all aspects from the initial data analysis and anomaly discovery to the accurate identification of attacks and the dynamic adjustment of defense strategies. This forms a complete security protection system that significantly improves the security and effectiveness of the honeypot system in the complex IoT environment. Figure 4 illustrates this sequential process, where each technique plays a key role. Unsupervised learning identifies potential threats, supervised learning models classify them, and reinforcement learning ultimately optimizes the honeypot's defense strategy.  
However, there are also challenges. The combination of multiple machine learning techniques requires a large amount of high-quality data for training and learning. In the IoT environment, collecting and labeling data is often difficult. The data may also contain noise and be incomplete. To solve these problems, data augmentation techniques can be adopted to increase the amount of data. Semi-supervised learning can be used to make better use of unlabeled data. Additionally, effective data cleaning and preprocessing algorithms need to be developed to improve data quality. Another challenge is the increase in model complexity and computational resource requirements. Combining multiple machine learning techniques can lead to more complex models that demand more computing power. In resource-constrained IoT devices and honeypot systems, this can be a problem. To address this, model compression techniques can be used to reduce the size of the models. Lightweight algorithms can be adopted to reduce computational burden. Edge computing  
Figure 4: Process with different machine learning techniques.  
can also be utilized to distribute the computational load. At the same time, optimizing algorithms and model structures can improve computational efficiency.  
In summary, the combination of supervised, unsupervised, and reinforcement learning techniques in IoT honeypots has great potential. By leveraging their complementary advantages and addressing the challenges, the security and performance of IoT honeypots can be enhanced, thus better protecting IoT systems from various security threats.