Machine Learning-Based Network Intrusion Detection System
Built a network IDS using supervised machine learning with SVM and Random Forest classifiers on the NSL-KDD dataset, achieving 92% accuracy and 80% F1-score with 6% false positive rate.
Key Achievements:
- Achieved 92% accuracy and 80% F1-score with 6% false positive rate after feature engineering and model optimization
- Used StandardScaler for normalization, SMOTE for class balancing, and 5-fold cross-validation for robust model evaluation
- Simulated attack traffic including DoS floods, brute-force attempts on SSH/RDP, port scans, and credential misuse \n