Enhancing IoT Security Development and Evaluation of a Predictive Machine Learning Model for Attack Detection

Abstract

The research aims to develop and evaluate a model for Internet of Things (IoT) attack identification utilizing IoT data from the Global Cyber Alliance’s (GCA) Automated IoT Defense Ecosystem (AIDE). In the growing landscape of IoT security, the need for enhanced predictive solutions is vital. Our research leverages an enormous dataset, overall historical data from various IoT devices and network interactions, to develop a model to identify potential security threats. The key to our methodology concerns exploratory data analysis, which is focused on understanding complex patterns and anomalies in IoT data. This step is vital for feature engineering, where we meticulously select and transform data attributes to advance the model’s predictive strength. The data pre-processing stage further improves the dataset, ensuring the model training and testing on high-quality, relevant data. Model development is a composite process in this research. We tried out a few machine-learning algorithms, finally selecting the one that exhibited outstanding performance in preliminary tests. The chosen model endured strict training, with a basis on balancing accuracy and validity to effectively predict IoT attacks in various scenarios. The evaluation of our model is as durable as its development. We utilized a range of metrics, including accuracy, precision, recall, and F1 score, to evaluate the model’s behavior overall. The results show that our model not only attains high accuracy but also maintains a notable level of precision in predicting IoT attacks, which is crucial in minimizing false positives. In conclusion, our research contributes to the enhancement of IoT security by providing a very effective predictive model for IoT attack detection.