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

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 I