The growing number of IoT devices in smart homes, healthcare, and industrial environments has led to an increase in potential vulnerabilities. This competition challenges participants to build intelligent models that can classify network traffic as either benign or malicious using the CIC-IoMT 2024 dataset.

The dataset contains labeled WiFi and MQTT network traffic generated in realistic IoT environments. The goal is to help advance research and practical solutions in network intrusion detection, especially in lightweight and real-time systems.

Participants are expected to apply the complete machine learning pipeline including preprocessing, feature selection or dimensionality reduction, and classification. You can use any model from traditional machine learning (e.g., Random Forest, XGBoost) to deep learning (e.g., MLP, CNN).

The test set is unlabeled. Your task is to predict the correct class label for each record in the test set and submit your predictions in the required format.