Optimizing IoT Intrusion Detection: A Comparative Analysis of XGBoost and Optimized Sequential Neural Networks
Optimizing IoT Intrusion Detection: A Comparative Analysis of XGBoost and Optimized Sequential Neural Networks The burgeoning Internet of Things (IoT) generates massive volumes of sensitive data, creating a critical need for robust cybersecurity measures. Machine learning (ML) and deep learning (DL) techniques offer a promising approach to anomaly-based intrusion detection, identifying unusual network behavior that signals potential threats. However, existing methods often struggle to effectively counter the sophisticated and evolving nature of modern cyberattacks, particularly concerning preprocessing optimization and hyperparameter…