Enhancing SIoT Security Through Advanced Machine Learning Techniques for Intrusion Detection

This study delves into the intricacies of SIoT networks, characterized by diverse data modalities, sensor data, device interactions, and social connections. In order to address evolving threats, a comprehensive approach is proposed, integrating advanced ML models-Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Logistic Regression (LR)- in order to detect intrusions in SIoT environments. The method encompasses rigorous data collection, preprocessing, feature selection, and model training. Performance evaluation reveals CNN + GAN's superiority with an 85% accuracy, surpassing other models. Detailed metrics include precision, accuracy, recall, ROC AUC, and F1-score, emphasizing the effectiveness of the proposed approach. This research significantly advances SIoT security, offering insights crucial for designing secure and resilient networks in the increasingly interconnected landscape.