# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 5 Findings
### Machine Learning Techniques  
With the rapid proliferation of IoT devices, network security issues have become increasingly prominent. IoT devices often face constraints such as low power, limited storage, and complex deployment environments, making it difficult to adopt traditional security measures [35]. In recent years, honeypot technology has gained widespread application in IoT security research. By simulating vulnerable environments, honeypots attract and record attack behaviors, providing data for the analysis and detection of attack patterns. However, the massive and complex datasets generated by honeypot systems are challenging to analyze quickly and accurately using traditional techniques [44]. As a result, machine learning techniques have been introduced into IoT honeypot systems to enhance the efficiency of attack detection and classification. Supervised learning techniques excel in detecting and classifying known attack patterns. These techniques rely on labeled data and learn the characteristics of known attack behaviors to identify similar patterns. The main advantages of supervised learning lie in its high detection accuracy and strong interpretability. For example, algorithms like Support Vector Machines (SVMs) and Random Forest (RF) can effectively distinguish between normal and malicious traffic [36]. Random forest, in particular, reduces the risk of overfitting by combining multiple decision trees, enabling efficient classification of complex traffic in IoT environments. Additionally, deep learning techniques such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) have been applied to traffic analysis, extracting complex spatiotemporal features to detect more sophisticated attacks [71]. One practical application is botnet attack detection. In a smart factory scenario, combining honeypot data with a random forest algorithm achieved 96% accuracy, significantly enhancing security [44]. Another study showed that a random forest-based model achieved an AUC of 0.93 in classifying honeypot servers, effectively distinguishing them from regular servers and reducing the risk of detection by attackers [37]. As shown in Table 4, supervised learning models such as random forest, implemented in Weka and R-Studio, demonstrated high accuracy (up to 0.96) and consistent precision/recall values. However, discrepancies in false positive rates (FPR of 0.219 vs. 0.2667) underscore the importance of tool selection in real-world deployments. Despite these strengths, supervised learning in IoT environments faces challenges. First, it requires a large amount of labeled data, which is costly and time-consuming to obtain [29]. Second, complex models (e.g., deep learning) demand high computational and storage resources, making them difficult to deploy on resource-constrained IoT devices [50]. Balancing detection accuracy with computational efficiency remains a critical challenge for supervised learning applications in IoT honeypots. Unsupervised learning does not rely on labeled data, making it suitable for discovering unknown attack patterns and abnormal behaviors. Its primary applications include anomaly detection and traffic clustering. Clustering algorithms like K-means and DBSCAN group traffic data to identify anomalies [45]. K-means clusters data points based on their distance from centroids, while DBSCAN uses density to group adjacent points, marking those in sparse regions as anomalies. Isolation forest, a popular unsupervised anomaly detection algorithm, uses tree-based models to identify outliers, performing well in detecting infrequent attack patterns. For instance, studies have shown that isolation forest effectively identifies anomalous traffic in IoT networks, uncovering new attack patterns without labeled data [36]. Dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE have also been applied to honeypot data analysis [32].