Detecting and Preventing Cyberattacks in Internet of Things (IoT) Systems
TL;DR
This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks that integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while reducing false-positive predictions.
Abstract
The rapid growth of Internet of Things (IoT) devices has significantly improved connectivity across smart environments. However, the resource-constrained nature of IoT devices and their limited built-in security mechanisms make them highly vulnerable to evolving cyberattacks. Traditional intrusion detection systems relying on signature-based or static rule sets are often ineffective against previously unseen attacks. This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks. The framework integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while reducing false-positive predictions. The ensemble outputs are combined using a Logistic Regression meta-classifier to generate the final threat score. Experimental evaluation using the UNSW-NB15 dataset demonstrates that Extreme Gradient Boosting achieved the highest individual Receiver Operating Characteristic-Area Under the Curve score of 0.91, while the proposed framework further enhances detection robustness through ensemble learning and automated prevention. A Flask-based monitoring dashboard provides real-time visualization of detection results, blocked IP addresses, alerts, and system performance.