Monitor malicious traffic requests using CTGAN and ICNN models with IDS-2017 dataset on AWS SageMaker MLOps for continuous training and deployment

CTGAN & ICNN Models: Use CTGAN to generate synthetic data for training and ICNN for deep learning-based traffic anomaly detection. Dataset: Use the IDS-2017 dataset to train the models. This dataset contains network traffic data with labeled malicious activity, ideal for identifying potential threats. AWS SageMaker MLOps: Integrate the solution with AWS SageMaker for: Continuous training of the models on new data. Deployment of trained models for real-time predictions. Monitoring the performance of the models. Continuous Deployment: Use SageMaker pipelines to automate training, evaluation, and deployment to production, ensuring the system remains up to date and accurate. Traffic Monitoring: Use the trained models to monitor incoming traffic, identifying and flagging suspicious requests. Scalability: Ensure the system can handle high traffic volumes and process data in real-time.