# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 5 Findings
### Machine Learning Classifiers
[PERSON] [30] shows how well random forest performs compare to the naive Bayes classifer  
Figure 3: Process flow for machine learning-based detection framework from the literature [65]. and K-nearest neighbors method. The aim of IoT security is to identify different types of network traffic attacks [34]. Overall, it can be said that the ensemble voting classifier is the most suitable for IoT, and the analysis option with machine learning is very broadly based. The S-Pot as a smart honeypot framework represents a cutting-edge solution for enhancing cybersecurity through advanced machine learning techniques [34]. Key components include data cleansing to remove noise and inconsistencies and feature selection to identify the most relevant characteristics for classifying attacks. Through data preprocessing and feature extraction, S-Pot ensures optimal input for its classifiers. The framework is designed with flexibility, allowing for the integration of modern machine learning techniques, including deep learning. Classification results are seamlessly passed to a dynamic rule configuration module, enhancing integration capabilities. Notably, S-Pot excels in the detection of new attack types, enabled by machine learning classifiers, achieving a high accuracy of 97 percent in attack detection using the J48 algorithm. Additionally, it minimizes false alarms, effectively reducing false negatives, making it a reliable and efficient solution for modern threat landscapes [34]. By combining these features, S-Pot demonstrates a robust and adaptable approach to addressing evolving cyber threats. The Hybrid Web Attack Detection Framework introduces an innovative Convolutional Neural Network (CNN)-based deep learning classifier tailored for IoT honeypots [54]. This deep learning classifier demonstrates superior performance with no signs of overfitting, ensuring reliable and efficient detection of web attacks. An integral component of the system is the Cookie Analysis Engine (CAE), which leverages deep learning to provide personalized analysis. The CAE is crucial for maintaining attacker sessions and enabling customized deception strategies. It functions by analyzing cookie fields and categorizing them as benign, suspicious, or malicious. However, its performance depends on the deep learning classifier for analyzing additional fields, highlighting a key limitation. An important observation is that many malicious requests contain seemingly benign cookie fields, underscoring the necessity of the deep learning classifier for comprehensive and accurate analysis [54]. By combining CNN-based classification with cookie analysis, this framework provides an effective and tailored solution for detecting web-based attacks in IoT honeypot environments. To further validate and compare the performance of different machine learning classifiers, several open-source implementations and benchmarks can be utilized. For instance, the bench-ml project on GitHub (version 2.0.1, developed by GitHub, Inc., located in San Francisco, CA, USA) provides a minimal benchmark for scalability, speed, and accuracy of commonly used open-source implementations of machine learning algorithms, including random forests, gradient boosted trees, and deep neural networks [72]. Additionally, the scikit-learn bench project benchmarks various implementations of machine learning algorithms across data analytics frameworks such as scikit-learn, DAAL4 PY, cuML, and XGBoost [72]. These resources offer valuable insights into the performance of different classifiers and can be instrumental in optimizing IoT honeypot systems. Overall, there are various classifier technologies for machine learning techniques that may be relevant for integration with IoT honeypots. These results can be seen as an extension of the machine learning method section.