Designed an advanced machine learning-based Intrusion Detection System (IDS) to enhance cybersecurity by identifying and mitigating network threats.
- Implemented ensemble learning techniques (Boosting and Bagging) for robust anomaly detection.
- Trained the model using the NSL-KDD 10 dataset, achieving 99% detection accuracy in identifying network intrusions.
- Conducted comprehensive feature engineering and utilized Recursive Feature Elimination (RFE) to optimize model performance and increase detection efficiency.